This research received no external funding. We thank Lisa Tiedemann, Ester Krist and Tobias Anke for technical support.

This study was performed according to the Declarations of Helsinki and approved by the Ethics Committee of the Aerztekammer Westfalen-Lippe (reference number 2019-456).
1. Study
<ol>
	<li>Instruct subjects on how to use the smartwatch for proper ECG recording.</li>
</ol>
2. Recording of a standard 12-lead ECG by a common device
<ol>
	<li>Use a common ECG device for standard ECG recording.</li>
	<li>Adjust the paper running speed to 50 mm/s.</li>
	<li>Perform the ECG recording after a 5 min resting period in a supine position.</li>
	<li>Place the right arm electrode near the right shoulder.</li>
	<li>Place the left arm electrode near the left shoulder.</li>
	<li>Place the right leg electrode near the right ankle.</li>
	<li>Place the left leg electrode near the left ankle.</li>
	<li>Place the V1 electrode in the fourth intercostal space at the right parasternal line.</li>
	<li>Place the V2 electrode in the fourth intercostal space at the left parasternal line.</li>
	<li>Place the V3 electrode between V2 and V4.</li>
	<li>Place the V4 electrode in the fifth intercostal space at the midclavicular line.</li>
	<li>Place the V5 electrode in the fifth intercostal space at the anterior axillary line.</li>
	<li>Place the V6 electrode in the fifth intercostal space at the mid-axillary line.</li>
	<li>Record a standard 12-lead ECG with the standard ECG device. 
	NOTE: The patient should not move during the ECG recording in order to prevent ECG artifacts.</li>
</ol>
3. Recording of Einthoven leads I-III by a smartwatch with ECG function
<ol>
	<li>Record smartwatch ECGs directly after recording the standard ECGs.</li>
	<li>Enable the application of the smartwatch for ECG recordings. A 30 s ECG will be recorded directly after proper skin contact with the smartwatch.</li>
	<li>Record Einthoven I by placing the back of the smartwatch on the left wrist and the right index finger on the crown (Figure 1A).</li>
	<li>Record Einthoven II by placing the back of the smartwatch on the left lower abdomen and the right index finger on the crown (Figure 1B).</li>
	<li>Record Einthoven III by placing the back of the smartwatch on the left lower abdomen and the left index finger on the crown (Figure 1C). 
	NOTE: The right and left index finger should not contact the skin of the left wrist or left lower abdomen for adequate ECG recording. The patient should not move during the ECG recording in order to prevent ECG artifacts.</li>
</ol>
4. Analysis of the ECGs
<ol>
	<li>Recorded smartwatch ECGs are digitally stored using the smartphone app.</li>
	<li>Use the "send PDF to your doctor" function to create a PDF document of every single smartwatch ECG lead. Print the digital smartwatch ECG on paper for comparison with the standard ECG on printed paper.</li>
	<li>Classify all recorded smartwatch ECGs as of moderate signal quality if at least three consecutive QRS-complexes show noise-free signal quality and there are no artifacts in the isoelectric lines between QRS-complexes.</li>
	<li>Classify smartwatch ECGs as of good signal quality if at least ten QRS-complexes show noise-free signal quality and there are no artifacts in isoelectric lines between QRS-complexes.</li>
</ol>
5. Statistical analysis
<ol>
	<li>Perform statistical analysis using IBM SPSS Statistics. 
	NOTE: Categorical variables are shown as absolute numbers and percentages. Continuous variables are presented as mean &#177; standard deviation. Differences of metric outcome variables were assessed by one way repeated analysis of variance (ANOVA) and paired t-test. In case of binary variables, the &#967;2-test was used.</li>
</ol>

<ol>
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P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rationale+and+design+of+a+large-scale,+app-based+study+to+identify+cardiac+arrhythmias+using+a+smartwatch:+The+Apple+Heart+Study.">Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study.</a> American Heart Journal. 207, 66-75 (2019).</li><li>Dorr, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+WATCH+AF+Trial:+SmartWATCHes+for+Detection+of+Atrial+Fibrillation.">The WATCH AF Trial: SmartWATCHes for Detection of Atrial Fibrillation.</a> JACC Clinical Electrophysiology. 5 (2), 199-208 (2019).</li><li>Hochstadt, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Continuous+heart+rate+monitoring+for+automatic+detection+of+atrial+fibrillation+with+novel+bio-sensing+technology.">Continuous heart rate monitoring for automatic detection of atrial fibrillation with novel bio-sensing technology.</a> Journal of Electrocardiology. 52, 23-27 (2019).</li><li>Baquero, G. A., Banchs, J. E., Ahmed, S., Naccarelli, G. V., Luck, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Surface+12+lead+electrocardiogram+recordings+using+smart+phone+technology.">Surface 12 lead electrocardiogram recordings using smart phone technology.</a> Journal of Electrocardiology. 48 (1), 1-7 (2015).</li><li>Desteghe, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Performance+of+handheld+electrocardiogram+devices+to+detect+atrial+fibrillation+in+a+cardiology+and+geriatric+ward+setting.">Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting.</a> Europace. 19 (1), 29-39 (2017).</li><li>Samol, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prevalence+of+unknown+atrial+fibrillation+in+patients+with+risk+factors.">Prevalence of unknown atrial fibrillation in patients with risk factors.</a> Europace. 15 (5), 657-662 (2013).</li><li>Nigolian, A., Dayal, N., Nigolian, H., Stettler, C., Burri, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnostic+accuracy+of+multi-lead+ECGs+obtained+using+a+pocket-sized+bipolar+handheld+event+recorder.">Diagnostic accuracy of multi-lead ECGs obtained using a pocket-sized bipolar handheld event recorder.</a> Journal of Electrocardiology. 51 (2), 278-281 (2018).</li><li>Foster, K. 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The authors have nothing to disclose.

Smart devices like smartphones and smartwatches are increasingly used in daily life and medical care1. These new devices and apps may have a significant impact on health awareness of the population, but their effective use needs to be tested in studies8. To the best of our knowledge, our group was the first to develop this method of single-lead ECG recordings corresponding to the conventional Einthoven ECG leads I-III using a smartwatch14.
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Smartwatches or other so-called "wearable devices" show increasing popularity and a steeply rising daily use in Western countries. Nearly 80% of US-Americans own a smartphone and more than 10% have a smartwatch1. Due to a photoplethysmographic sensor using LED-light and photodiodes, some smartwatches can record pulse frequency and irregularities1. This feature enables the detection of arrhythmias, especially AF, with high diagnostic accuracy2,3. For authentic ECG arrhythmia detection, portable, handheld, and wearable ECG devices have been developed to enable smartphone-assisted ECG recordings. Nevertheless, these devices allow patient-activated recording of electrocardiograms only if the patients&rsquo; compliance for carrying the ECG device is extremely high4,5,6,7.
Thus, the optimal tool for a patient&rsquo;s medical surveillance would be a smart device for daily use. Some last generation smartwatches enable a single-lead ECG recording comparable to bipolar lead Einthoven I from a standard 12-channel ECG using the backside of the watch as the positive and the crown as the negative electrode8. ECG recording is patient-controlled and activated if symptoms occur. Thereafter, an application creates a PDF document for further analysis by a healthcare professional. Nevertheless, using only a single-lead ECG for discrimination of P waves for diagnosis of sinus rhythm is sometimes insufficient9 for detection of the P wave and often multiple ECG leads are required5. In addition, multichannel ECG recording is mandatory for diagnosis of most acute or chronic structural heart diseases like myocardial infarction (MI), pulmonary embolism, or signs of acute heart failure.
More than 100 years ago, Einthoven developed a method for recording of a bipolar three- channel ECG10. This three-channel ECG offers the opportunity to identify the electrical heart axis and possibly the myocardial ischemia as well, especially in inferior regions of the myocardium11. Therefore, in clinical daily practice bipolar Einthoven leads I-III are essential parts of the 12-lead ECG and enable heart rhythm determination or detection of myocardial ischemia.
Early diagnosis and especially early treatment of myocardial infarction has improved substantially during recent decades. Nevertheless, especially early after the onset of symptoms, many patients hesitate to contact professional help. Thus, first medical contact and initiation of adequate treatment is often delayed12. Registration and transmission of a patient-directed ECG early after the onset of symptoms might accelerate specific treatment and thus enable a better patient outcome7. Until now, ischemia detection by smart devices is limited, because mainly single-lead (Einthoven I), or as in our study, maximal three-lead (Einthoven I-III) ECGs can be recorded, which only represent a limited area of the myocardium.
Several studies used patient-directed devices like portable ECG recorders, smartphones, and very recently smartwatches, for detection of AF in heart patients1,2,5,9. The Apple Heart Study and the WATCH AF trial used the photoplethysmographic LED-light sensor of the smartwatch for detection of an irregular or variable pulse, which correlates with arrhythmia like AF1,2. Insufficient signal quality was the limiting factor in these trials, leading to a high dropout rate2. Another smartwatch trial used photoplethysmography for AF detection, but also showed reduced diagnostic accuracy compared to regular ECGs13.
The detection of AF by the registration of pulse irregularities is the limiting factor of photoplethysmography, because heartbeat variabilities due to extra systoles or sinus arrhythmia may also cause pulse irregularities. Thus, recording of an ECG by a smartphone or smartwatch may increase the sensitivity and specificity of arrhythmia detection. Several smartphone compatible devices can record a bipolar single-lead ECG simulating Einthoven lead I5,9. In one study, a bipolar smartphone ECG device was used for AF screening9. In this trial, a small voltage of P waves in lead I led to incorrect AF determination, a limitation when only a single-lead ECG is available9. ECG devices for AF screening were also tested in hospitalized patients on cardiologic and geriatric wards5. Diagnostic accuracy of the automated algorithms was only suboptimal and additional 12-lead ECGs were often mandatory. Most of these devices have the limitation of only one ECG lead recording (Einthoven I), which is not always sufficient to ensure arrhythmia or repolarization detection.
Only one small case series of five patients demonstrated that a conventional 12 lead ECG is recordable by a conventional bipolar smartphone device after modification for unipolar lead recordings with ECG tabs and wires with alligator clips4. They showed ECG recordings with good signal quality, but the limiting factor is the need for device modifications that complicates patient-directed self-ECG recording.
In contrast, we performed the first study for recording an ECG with a smartwatch with the three bipolar Einthoven leads as a proof of concept in healthy subjects. We were able to show a high grade of consistency between the smartwatch leads and the Einthoven leads from a standard ECG using the following simple protocol.

<table><tbody><tr><td>Apple Watch Series 4</td><td>Apple</td><td>Smartwatch with bipolar ECG function</td><td></td></tr><tr><td>IBM SPSS Statistics</td><td>IBM</td><td>version 25 for Mac</td><td></td></tr><tr><td>MAC 5500</td><td>GE Healthcare</td><td>Standard 12 channel ECG device</td><td></td></tr></tbody></table>

patient directed recording of a bipolar three-lead electrocardiogram using a smartwatch with ecg function

Cardiac arrhythmias and cardiovascular diseases are a major public health problem in developed countries. A major goal in preventive medicine is the reduction of cardiovascular death by early detection of atrial fibrillation (AF), which may cause stroke, or early detection of life-threatening myocardial ischemia in acute coronary syndrome. Detection of arrhythmia is often challenging if symptoms occur when patients have no chance for immediate electrocardiogram (ECG) diagnostic testing, or if the observation time period is short or an immediate visit to their doctor is not possible. Smartwatches and other wearable devices are able to record a single lead ECG recording, but a single lead ECG is often not sufficient for diagnosis of cardiovascular disorders. Even diagnosis of AF can be difficult with only information from a single lead bipolar ECG. Some smart devices use photoplethysmography for detection of cardiac rhythm, but this technique can only give indirect hints of the underlying cardiac rhythm, is prone to interferences, and cannot be used for detection of myocardial ischemia. A three-lead bipolar ECG like the Einthoven leads used in regular ECGs can add useful information concerning arrhythmia detection or even diagnosis of other cardiovascular diseases like ischemia. Therefore, we describe a protocol for the patient-directed recording of an Einthoven three-lead ECG using a smartwatch.

In a cohort of 100 healthy subjects (66 female) we investigated the feasibility of our smartwatch recording protocol. The subjects' characteristics are shown in Table 1. After a short tutorial all volunteers managed the ECG recording procedure with the smartwatch. All 300 smartwatch ECGs were useable for further analysis with at least adequate signal quality for diagnostics purposes. Of the total ECGs, 277 (92%) were of good quality and 23 (8%) of moderate signal quality. Three blinded cardiologists were...

Watch this Scientific Journal Video about Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function at JoVE.com

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

We describe a protocol for the patient-directed registration of a three lead bipolar electrocardiogram by a smartwatch that functions identically to the Einthoven leads from standard electrocardiograms. This enables patients to record electrocardiograms on their own immediately after the onset of symptoms.

Cardiac arrhythmias and cardiovascular diseases are a major public health problem in developed countries. A major goal in preventive medicine is the ...

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Medicine

8.5K Views.  Ruhr University Bochum. We describe a protocol for the patient-directed registration of a three lead bipolar electrocardiogram by a smartwatch that functions identically to the Einthoven leads from standard electrocardiograms. This enables patients to record electrocardiograms on their own immediately after the onset of symptoms.

Video: Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches for emotional regulation and optimizing compensatory reserve mechanisms. This protocol is designed to not only teach mind-body awareness but also to provide feedback utilizing physiological data and survey results. The primary findings of this study showed that heart coherence and alpha variables were significantly correlated after four weeks of the breath-based meditation stress protocol. Meditation and rhythmic breathing produced significant increases in alpha brain activity. These brain physiological responses conformed to the Pleth Variability Index (PVI) changes, suggesting the ability of the human body to enter into a meditative state and effectively manage stress. When assessed after four weeks of daily practicing the techniques employed in the stress management protocol, based on the Five Facet Mindfulness Questionnaire, subjects improved in applying mindfulness skills. The overall mindfulness score, Pleth variability index (PVI), and perfusion index (PI) increased after the 4-week intervention period. Results from electroencephalography (brain waves) were consistent with a meditative state during the post-study follow-up session. This provides evidence that wearable devices are feasible for data collection during a breath-based stress management intervention. This protocol can be easily and efficiently implemented into any study design in which physiological data are desired in a non-laboratory-based setting.

Stress is an unavoidable and persistent component of life and holistic approaches for its management are being considered. A standardized methodology was created to demonstrate the feasibility of a breath-based stress management protocol that can be used with commercially available portable technology.

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches ...

Behavior

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Each year, an estimated 785,000 Americans will have a new coronary attack, or acute coronary syndrome (ACS). The pathophysiology of ACS involves rupture of an atherosclerotic plaque; hence, treatment is aimed at plaque stabilization in order to prevent cellular death. However, there is considerable debate among clinicians, about which treatment pathway is best: early invasive using percutaneous coronary intervention (PCI/stent) when indicated or a conservative approach (i.e., medication only with PCI/stent if recurrent symptoms occur).
There are three types of ACS: ST elevation myocardial infarction (STEMI), non-ST elevation MI (NSTEMI), and unstable angina (UA). Among the three types, NSTEMI/UA is nearly four times as common as STEMI. Treatment decisions for NSTEMI/UA are based largely on symptoms and resting or exercise electrocardiograms (ECG). However, because of the dynamic and unpredictable nature of the atherosclerotic plaque, these methods often under detect myocardial ischemia because symptoms are unreliable, and/or continuous ECG monitoring was not utilized.
Continuous 12-lead ECG monitoring, which is both inexpensive and non-invasive, can identify transient episodes of myocardial ischemia, a precursor to MI, even when asymptomatic. However, continuous 12-lead ECG monitoring is not usual hospital practice; rather, only two leads are typically monitored. Information obtained with 12-lead ECG monitoring might provide useful information for deciding the best ACS treatment.
Purpose. Therefore, using 12-lead ECG monitoring, the COMPARE Study (electroCardiographic evaluatiOn of ischeMia comParing invAsive to phaRmacological trEatment) was designed to assess the frequency and clinical consequences of transient myocardial ischemia, in patients with NSTEMI/UA treated with either early invasive PCI/stent or those managed conservatively (medications or PCI/stent following recurrent symptoms). The purpose of this manuscript is to describe the methodology used in the COMPARE Study.
Method. Permission to proceed with this study was obtained from the Institutional Review Board of the hospital and the university. Research nurses identify hospitalized patients from the emergency department and telemetry unit with suspected ACS. Once consented, a 12-lead ECG Holter monitor is applied, and remains in place during the patient's entire hospital stay. Patients are also maintained on the routine bedside ECG monitoring system per hospital protocol. Off-line ECG analysis is done using sophisticated software and careful human oversight.

Continuous 12-lead electrocardiographic (ECG) monitoring can identify transient myocardial ischemia, even when asymptomatic, among patients with suspected acute coronary syndrome (ACS). In this article we describe our method for initiating patient monitoring using a Holter device, downloading the ECG data for off-line analysis, and how to utilize the ECG software to identify transient ischemia.

Each year, an estimated 785,000 Americans will have a new coronary attack, or acute coronary syndrome (ACS). The pathophysiology of ACS involves rupture ...

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to join existing programs at cardiac rehabilitation centers. The unique remote patient monitoring system presented here enables healthcare providers to monitor CR patients at home in real-time and at low cost. The system combines mobile technology, artificial intelligence, and supportive services, expanding the delivery of medical care to the patient&#39;s home. The primary aim of the study is to increase the long-term adherence to physical activity in patients who participate in CR via the addition of a home-based digitally monitored CR component to the standard CR program in patients with ischemic heart disease (IHD), with the idea of forming new habitual health behaviors and increasing the long-term motivation for physical exercise (PE) habits at home. Secondary aims are to assess the program&#39;s impact on the physical activity level measured by average steps per day, minutes of exercise per week, blood pressure, metabolic parameters, body mass index, and waist-to-hip ratio, as well as a quality-of-life (QoL) questionnaire.The study has two arms: (1) home-based monitored exercise using a smart digital garment and wristband, in addition to motivation and reinforcement via text messages; (2) standard CR facility-based exercise. The study design is a randomized, controlled trial comparing standard CR to a home-based monitoring and reinforcement program. The study program is designed for 12 weeks.Clinical tests and anthropometric measurements are performed before and after the study, measuring height, weight, waist circumference, visceral fat and body mass index (BMI), blood pressure, and HbA1c and lipid profile. Patients have to complete a baseline survey including socio-demographic characteristics and QoL questionnaire SF-36. At the end of the study, patients complete a survey regarding the use of the smart digital garment&#39;s benefits and usability. The study is currently underway.

The aim is to promote a new approach to cardiac rehabilitation (CR), using a unique remote patient monitoring system that will enable healthcare providers to monitor CR patients at home at low cost, with the intention of making CR services more accessible and improving compliance. The study is currently underway.

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to ...

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

The aim of this study was to generate a method for calculating heart rate variability (HRV) from electrocardiogram (ECG) waveforms. The waveforms were recorded by a HR monitor that participants (youth with cerebral palsy (CP)) wore during active video game (AVG) sessions. The AVG sessions were designed to promote physical activity and fitness (aerobic performance) in participants. The goal was to evaluate the feasibility of AVGs as a physical therapy (PT) intervention strategy. The maximum HR (mHR) was determined for each participant and the Target Heart Rate Zone (THRZ) was calculated for each of three exercise phases in the 20 min AVG session: (warm-up at 40-60% mHR, conditioning at 60-80% mHR, and cool down at 40-60% mHR). Each participant played three 20 min games during the AVG session. All games were played while sitting on a bench because many youth with CP cannot stand for extended periods of time. Each game condition differed with participants using hand icons only, hand and feet icons together or feet icons only to collect objects. The objective of the game (called KOLLECT) is to collect objects to gain points and avoid hazards to not lose points. Hazards were used in the warm-up and cool down phases only to promote slower, controlled movement to maintain HR in the target heart rate zone (THRZ). There were no hazards in the conditioning phase to promote higher levels and more intense physical activity. Analytic methods were used to generate HRV (selected time-domain and frequency-domain measures) from ECG data to examine aerobic workload. Recent applications of HRV indicate that short-term measurements (5 min bouts) are appropriate and that HRV biofeedback may help improve symptoms and the quality of life in a variety of health conditions. Although HR is a well-accepted clinical measure to examine aerobic performance and intensity in PT interventions, HRV may provide information of the autonomic system functions, recovery and adaptation during AVG sessions.

This protocol describes a method for calculating Heart Rate Variability (HRV) from electrocardiogram (ECG) waveforms. Waveforms from continuous heart rate (HR) recordings during active video game (AVG) sessions were used to measure the aerobic performance of youth with cerebral palsy (CP).

The aim of this study was to generate a method for calculating heart rate variability (HRV) from electrocardiogram (ECG) waveforms. The waveforms were ...

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU), on smartphones and smartwatches to collect data on users' real-time psychological and physiological responses and environmental information. A flexible and integrated research method was proposed because it can be difficult to measure multi-dimensional aspects of personal data in on-site studies in landscape and outdoor recreation research. An on-site study conducted in 2020 at the National Taiwan University campus was used as an application example. A dataset of 385 participants was used after excluding invalid samples. During the experiment, participants were asked to walk around campus for 30 min when their heart rate and psychological-scale items were measured, together with several environmental metrics. This work aimed to provide a possible solution to help on-site studies track real-time human responses that match ambient factors. Due to the app's flexibility, its use on wearable devices shows excellent potential for multidisciplinary research studies.

The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the ...

Environment

Optimizing Deep Brain Stimulation for Patients with Neurological Disorders

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

Adaptive deep brain stimulation (aDBS) shows promise for improving treatment for neurological disorders such as Parkinson's disease (PD). aDBS uses symptom-related biomarkers to adjust stimulation parameters in real-time to target symptoms more precisely. To enable these dynamic adjustments, parameters for an aDBS algorithm must be determined for each individual patient. This requires time-consuming manual tuning by clinical researchers, making it difficult to find an optimal configuration for a single patient or to scale to many patients. Furthermore, the long-term effectiveness of aDBS algorithms configured in-clinic while the patient is at home remains an open question. To implement this therapy at large scale, a methodology to automatically configure aDBS algorithm parameters while remotely monitoring therapy outcomes is needed. In this paper, we share a design for an at-home data collection platform to help the field address both issues. The platform is composed of an integrated hardware and software ecosystem that is open-source and allows for at-home collection of neural, inertial, and multi-camera video data. To ensure privacy for patient-identifiable data, the platform encrypts and transfers data through a virtual private network. The methods include time-aligning data streams and extracting pose estimates from video recordings. To demonstrate the use of this system, we deployed this platform to the home of an individual with PD and collected data during self-guided clinical tasks and periods of free behavior over the course of 1.5 years. Data were recorded at sub-therapeutic, therapeutic, and supra-therapeutic stimulation amplitudes to evaluate motor symptom severity under different therapeutic conditions. These time-aligned data show the platform is capable of synchronized at-home multi-modal data collection for therapeutic evaluation. This system architecture may be used to support automated aDBS research, to collect new datasets and to study the long-term effects of DBS therapy outside the clinic for those suffering from neurological disorders.

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring 

The protocol shows a prototype of the at-home multi-modal data collection platform that supports research optimizing adaptive deep brain stimulation (aDBS) for people with neurological movement disorders. We also present key findings from deploying the platform for over a year to the home of an individual with Parkinson's disease.

Adaptive deep brain stimulation (aDBS) shows promise for improving treatment for neurological disorders such as Parkinson's disease (PD). aDBS uses ...

Neuroscience

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Regular physical exercise is a major contributor to cardiovascular health, influencing various metabolic as well as electrophysiological processes. However, in certain cardiac diseases such as inherited arrhythmia syndromes, e.g., arrhythmogenic cardiomyopathy (ACM) or myocarditis, physical exercise may have negative effects on the heart leading to a proarrhythmogenic substrate production. Currently, the underlying molecular mechanisms of exercise-related proarrhythmogenic remodeling are largely unknown, thus it remains unclear which frequency, duration, and intensity of exercise can be considered safe in the context of disease(s).
The proposed method allows to study proarrhythmic/antiarrhythmic effects of physical exercise by combining treadmill training with real-time monitoring of the ECG. Implantable telemetry devices are used to continuously record the ECG of freely moving mice over a period of up to 3 months both at rest and during treadmill training. Data acquisition software with its analysis modules is used to analyze basic ECG parameters such as heart rate, P wave duration, PR interval, QRS interval, or QT duration at rest, during and after training. Furthermore, heart rate variability (HRV) parameters and occurrence of arrhythmias are evaluated. In brief, this manuscript describes a step-by-step approach to experimentally explore exercise induced effects on cardiac electrophysiology, including potential proarrhythmogenic remodeling in mouse models.

Electrocardiogram (ECG) is the key variable to understanding cardiac electrophysiology. Physical exercise has beneficial effects but may also be harmful in the context of cardiovascular diseases. This manuscript provides a method of recording real-time ECG during exercise, which can serve to investigate its effects on cardiac electrophysiology in mice.

Regular physical exercise is a major contributor to cardiovascular health, influencing various metabolic as well as electrophysiological processes. ...

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

This study aims to validate the accuracy of low-cost fitness smartwatches by comparing their data with gold-standard measurements for cardiovascular and physical activity parameters. The study enrolled 50 subjects, 26 undergoing validation testing for heart rate, blood oxygen saturation (SpO2), and sleep data against polysomnography (PSG). Additionally, 24 subjects participated in the 3-Minute Walk Test (3MWT) and Stairs Climbing (SC), with step counts validated against manual video calculations. Results showed no significant difference between the device&#39;s measurements and gold standard values for shallow sleep, deep sleep, REM time, mean heart rate, minimum heart rate, and SpO2. However, the device significantly underestimated manually counted steps (p = 0.009 (3MWT); p = 0.012 (SC)), total sleep duration (p = 0.004), and wake time (p = 8.94&#160;&#215; 10-8) while overestimating maximum heart rate (p = 0.011). These findings highlight the importance of accurate validation and interpretation of wearable device data in clinical contexts. Given these limitations, excluding the device&#39;s readings in future analyses is recommended to maintain data reliability and research integrity. This study underscores the need for ongoing validation and improvement of wearable technology to ensure its reliability and effectiveness in healthcare.

Wearable technology has low cost and offers convenient monitoring of physiological data. However, the accuracy and reliability of these devices require cautious assessment to ensure their effectiveness and safety for users. This report describes the validation process of a commercial smartwatch in monitoring physiological data and physical activity.

This study aims to validate the accuracy of low-cost fitness smartwatches by comparing their data with gold-standard measurements for cardiovascular and ...

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Rapid, safe, and effective cardiac mapping is critical for the management of complex arrhythmias, yet current methods face significant limitations. The 12-lead electrocardiogram (ECG), while essential for initial diagnosis, lacks the spatial resolution and depth needed to guide advanced procedures such as catheter ablation or cardiac resynchronization therapy device implantation. On the other hand, invasive mapping techniques provide detailed electrical activity but require multiple catheter placements, increasing procedural risks and complexity. These methods are time-consuming, expensive, and offer limited real-time assessment, especially in dynamic arrhythmias like atrial fibrillation (AF), unstable arrhythmia, and cardiac resynchronization therapy (CRT).
This study introduces a noninvasive, imageless electrocardiographic imaging (Imageless-ECGI) system designed to complement traditional methods by providing real-time, beat-to-beat cardiac maps. Without the need for pre-procedural imaging, this system captures high-resolution electrical activity across the entire heart, offering a safer and more efficient alternative to invasive mapping. By combining the immediacy of surface recordings with the spatial accuracy of modern computational models, the Imageless-ECGI system bridges the gap between traditional ECG and invasive mapping, potentially transforming the workflow in electrophysiology labs.

This study introduces a novel approach for real-time cardiac mapping using a noninvasive, imageless electrocardiographic imaging system. This system enables the acquisition of electrophysiological cardiac maps without requiring pre-procedural computed tomography or magnetic resonance imaging scans, enabling efficient guidance for cardiac procedures such as ablation and cardiac resynchronization therapy implants.

Rapid, safe, and effective cardiac mapping is critical for the management of complex arrhythmias, yet current methods face significant limitations. The ...

Acquisition and Analysis of an ECG (electrocardiography) Signal

Source: <a href="https://www.jove.com/author/Peiman_Shahbeigi-Roodposhti">Peiman Shahbeigi-Roodposhti</a> and Sina Shahbazmohamadi, Biomedical Engineering Department, University of Connecticut, Storrs, Connecticut
An electrocardiograph is a graph recorded by electric potential changes occurring between electrodes placed on a patient&#39;s torso to demonstrate cardiac activity. An ECG signal tracks heart rhythm and many cardiac diseases, such as poor blood flow to the heart and structural abnormalities. The action potential created by contractions of the heart wall spreads electrical currents from the heart throughout the body. The spreading electrical currents create different potentials at points in the body, which can be sensed by electrodes placed on the skin. The electrodes are biological transducers made of metals and salts. In practice, 10 electrodes are attached to different points on the body. There is a standard procedure for acquiring and analyzing ECG signals. A typical ECG wave of a healthy individual is as follows:
<img alt="Figure 1" src="/files/ftp_upload/10473/10473fig1v2.jpg" /> 
Figure 1. ECG wave.
The &#34;P&#34; wave corresponds to atrial contraction, and the &#34;QRS&#34; complex to the contraction of the ventricles. The &#34;QRS&#39; complex is much larger than the &#34;P&#34; wave due to the relative dfference in muscle mass of the atria and ventricles, which masks the relaxation of the atria. The relaxation of the ventricles can be seen in the form of the &#34;T&#34; wave.
There are three main leads responsible for measuring the electrical potential difference between arms and legs, as shown in Figure 2. In this demonstration, one of the limb leads, lead I, will be examined, and the electrical potential difference between two arms will be recorded. As in all ECG lead measurements, the electrode connected to the right leg is considered the ground node. An ECG signal will be acquired using a biopotential amplifier and then displayed using instrumentation software, where a gain control will be created to adjust its amplitude. Finally, the recorded ECG will be analyzed.
<img alt="Figure 2" src="/files/ftp_upload/10473/10473fig2v2.jpg" /> 
Figure 2. ECG limb leads.