Benefits of early rhythm control of atrial fibrillation.

Smartwatches that support the recording of a single-lead electrocardiogram (ECG) are increasingly being used beyond the wrist, by placement on the ankle and on the chest. However, the reliability of frontal and precordial ECGs other than lead I is unknown. This clinical validation study assessed the reliability of an Apple Watch (AW) to obtain conventional frontal and precordial leads as compared to standard 12-lead ECGs in both subjects without known cardiac anomalies and patients with underlying heart disease. In 200 subjects (67% with ECG anomalies), a standard 12-lead ECG was performed, followed by AW recordings of the standard Einthoven leads (leads I, II, and III) and precordial leads V1, V3, and V6. Seven parameters (P, QRS, ST, and T-wave amplitudes, PR, QRS, and QT intervals) were compared through a Bland–Altman analysis, including the bias, absolute offset, and 95% limits of agreement. AW-ECGs recorded on the wrist but also beyond the wrist had similar durations and amplitudes compared to standard 12-lead ECGs. Significantly greater amplitudes were measured by the AW for R-waves in precordial leads V1, V3, and V6 (+0.094 mV, +0.149 mV, +0.129 mV, respectively, all p < 0.001), indicating a positive bias for the AW. AW can be used to record frontal, and precordial ECG leads, paving the way for broader clinical applications.

/pdf/using-a-smartwatch-to-record-precordial-electrocardiograms-a-15idmvu2.pdf

Using a Smartwatch to Record Precordial Electrocardiograms: A Validation Study

When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events. 

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

Human interaction with machines can be made easy, comfortable, and accessible by introducing user-friendly interfaces. In the case of wearable devices, their sensors and other interfacing elements are very well within the proximity of users. Since biopotential signals can be accessed from the surface of the human skin, users can have seamless interaction with wearable human-computer interactive devices. Rigid interfaces can hinder the user experience, and therefore, the need for soft biopotential interfaces is important. Imperceptible and unobtrusive soft biopotential interfaces will drastically enhance many aspects of human-computer interaction. This paper reviews the use of soft, flexible, and stretchable biopotential interfaces in wearable human-machine interactive devices. Additionally, attention is brought to the scope of other possible applications of soft biopotential interfaces in wearable devices.

Prospects of soft biopotential interfaces for wearable human-machine interactive devices and applications

Electrocardiogram (ECG) signal analysis plays a crucial role in diagnosing and monitoring various cardiac diseases. Automatic ECG beat classification is necessary to analyze long-term ECG recordings. The major limitations of the traditional automatic ECG beat classification approaches are the constraints of hand-crafted feature extraction, the requirement of an extensive training dataset, dealing with the ECG signal as an image, and poor performance in detecting supraventricular ectopic and ventricular (S and V) beats. To overcome the above-mentioned difficulties, a novel approach to ECG signal classification based on deep feature extraction with minimum complexity along with random forest is proposed in this work. Three different individual blocks are designed with convolutional neural networks (CNN), residuals, and bi-directional gated recurrent units (Bi-GRU) to extract distributed representative, hierarchical & condensed, and long-term dependency features. These extracted features are used to form deep features with the help of concatenation and fusion techniques. The resulting features are able to capture both the morphology and temporal dynamics of the ECG signal. These features are more effective in identifying different types of arrhythmias, predicting future cardiac events, and filtering out noise and artifacts. The unique nature of the features obtained by combining CNN, residual blocks, and Bi-GRU enables a more comprehensive and accurate analysis of the ECG signal, which is particularly important for diagnosing and monitoring cardiac abnormalities. Finally, the extracted deep feature set is utilized to train and test the random forest algorithm. The proposed approach was evaluated on three publicly available datasets and achieved better performance with an overall accuracy of more than 98.00%. Our approach outperforms existing literature by providing a more accurate classification of ECG signals. 

Multi-stream Bi-GRU network to extract a comprehensive feature set for ECG signal classification

The use of smartwatch electrocardiogram beyond arrhythmia detection.

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The use of smartwatch electrocardiogram beyond arrhythmia detection.