Existing HRV toolboxes do not include standardized preprocessing, signal quality indices (for noisy segment removal), and abnormal rhythm detection and are therefore likely to lead to significant errors in the presence of moderate to high noise or arrhythmias. We therefore describe the inclusion of validated tools to address these issues. We also make recommendations for default values and testing/reporting.

Objective Heart rate variability (HRV) characterizes changes in autonomic nervous system function and varies with posttraumatic stress disorder (PTSD). In this study we developed a classifier based on heart rate (HR) and HRV measures, and improved classifier performance using a novel HR-based window segmentation. Approach Single-channel ECG data were collected from 23 subjects with current PTSD, and 25 control subjects with no history of PTSD over 24 h. RR intervals were derived from these data, cleaned, and used to calculate HR and HRV metrics. These metrics were used as features in a logistic regression classifier. Performance was assessed via repeated random sub-sampling validation. To reduce noise and activity-related effects, we calculated features from five non-overlapping ten-minute quiescent segments of RR intervals defined by lowest HR, as well as random ten-minute segments as a control. Main Results Using a combination of the four most predictive features derived from quiescent segments we achieved a median area under the receiver operating curve (AUC) of 0.86 on out-of-sample test set data. This was significantly higher than the AUC using 24 h of data (0.72) or random segments (0.67). Significance These results demonstrate our segmentation approach improves the classification of PTSD from HR and HRV measures, and suggest the potential for tracking PTSD illness severity via objective physiological monitoring. Future studies should prospectively evaluate if classifier output changes significantly with worsening or effective treatment of PTSD.

Objective: Heart failure (HF) can be difficult to diagnose by physical examination alone. We examined whether wristband technologies may facilitate more accurate bedside testing. Approach: We studied on a cohort of 97 monitored in-patients and performed a cross-sectional analysis to predict HF with data from the wearable and other clinically available data. We recorded photoplethysmography (PPG) and accelerometry data using the wearable at 128 samples per second for 5 min. HF diagnosis was ascertained via chart review. We extracted four features of beat-to-beat variability and signal quality, and used them as inputs to a machine learning classification algorithm. Main results: The median [interquartile] age was 60 [51 68] years, 65% were men, and 54% had heart failure; in addition, 30% had acutely decompensated HF. The best 10-fold cross-validated testing performance for the diagnosis of HF was achieved using a support vector machine. The waveform-based features alone achieved a pooled test area under the curve (AUC) of 0.80; when a high-sensitivity cut-point (90%) was chosen, the specificity was 50%. When adding demographics, medical history, and vital signs, the AUC improved to 0.87, and specificity improved to 72% (90% sensitivity). Significance: In a cohort of monitored in-patients, we were able to build an HF classifier from data gathered on a wristband wearable. To our knowledge, this is the first study to demonstrate an algorithm using wristband technology to classify HF patients. This supports the use of such a device as an adjunct tool in bedside diagnostic evaluation and risk stratification.

No abstract

We have developed and applied new methods to estimate the functional life of miniature, implantable, wireless electronic devices that rely on non-hermetic, adhesive encapsulants such as epoxy. A comb pattern board with a high density of interdigitated electrodes (IDE) could be used to detect incipient failure from water vapor condensation. Inductive coupling of an RF magnetic field was used to provide DC bias and to detect deterioration of an encapsulated comb pattern. Diodes in the implant converted part of the received energy into DC bias on the comb pattern. The capacitance of the comb pattern forms a resonant circuit with the inductor by which the implant receives power. Any moisture affects both the resonant frequency and the Q-factor of the resonance of the circuitry, which was detected wirelessly by its effects on the coupling between two orthogonal RF coils placed around the device. Various defects were introduced into the comb pattern devices to demonstrate sensitivity to failures and to correlate these signals with visual inspection of failures. Optimized encapsulation procedures were validated in accelerated life tests of both comb patterns and a functional neuromuscular stimulator under development. Strong adhesive bonding between epoxy and electronic circuitry proved to be necessary and sufficient to predict 1 year packaging reliability of 99.97% for the neuromuscular stimulator.