Heart rate variability (HRV) analysis is a noninvasive tool widely used to assess autonomic nervous system state. The market for wearable devices that measure the heart rate has grown exponentially, as well as their potential use for healthcare and wellbeing applications. Still, there is a lack of validation of these devices. In particular, this work aims to validate the Apple Watch in terms of HRV derived from the RR interval series provided by the device, both in temporal (HRM (mean heart rate), SDNN, RMSSD and pNN50) and frequency (low and high frequency powers, LF and HF) domain. For this purpose, a database of 20 healthy volunteers subjected to relax and a mild cognitive stress was used. First, RR interval series provided by Apple Watch were validated using as reference the RR interval series provided by a Polar H7 using Bland-Altman plots and reliability and agreement coefficients. Then, HRV parameters derived from both RR interval series were compared and their ability to identify autonomic nervous system (ANS) response to mild cognitive stress was studied. Apple Watch measurements presented very good reliability and agreement (>0.9). RR interval series provided by Apple Watch contain gaps due to missing RR interval values (on average, 5 gaps per recording, lasting 6.5 s per gap). Temporal HRV indices were not significantly affected by the gaps. However, they produced a significant decrease in the LF and HF power. Despite these differences, HRV indices derived from the Apple Watch RR interval series were able to reflect changes induced by a mild mental stress, showing a significant decrease of HF power as well as RMSSD in stress with respect to relax, suggesting the potential use of HRV measurements derived from Apple Watch for stress monitoring.

This paper presents a complete study of wide-area wireless ECG transmission for real-time cardiac tele-monitoring taking into account both technical and clinical aspects, in order to provide recommendations for real-time monitoring considering both channel parameters and the tolerance of cardiologists to the effects of interruptions introduced during transmission. By using extensive wireless simulated scenarios, the compressed ECG signal is monitored on reception. A new protocol [real-time ECG transmission protocol, reliable ECG transmission protocol (RETP)] is used to perform the retransmissions of erroneous packets, introducing a monitoring buffer that mitigates possible negative effects. Assessments by cardiologists have shown that the maximum percentage of time for which the monitoring process could be stopped without their feeling uncomfortable is around 15% with a maximum monitoring delay of 3 or 4 s, depending on the scenario in question. Taking into account these values and the results obtained in the simulations, it is a straightforward step to obtain working areas for the wireless channel parameters where transmission is not recommended from a clinical point of view.

The photoplethysmogram (PPG) is a biomedical signal that can be used to estimate volumetric blood flow changes in the peripheral circulation. During the past few years, several works have been published in order to assess the potential for PPGs to be used in biometric authentication systems, but results are inconclusive. In this paper we perform an analysis of the feasibility of using the PPG as a realistic biometric alternative in the long term. Several feature extractors (based on the time domain and the Karhunen–Loève transform) and matching metrics (Manhattan and Euclidean distances) have been tested using four different PPG databases (PRRB, MIMIC-II, Berry, and Nonin). We show that the false match rate (FMR) and false non-match rate (FNMR) values remain constant in different time instances for a selected threshold, which is essential for using the PPG for biometric authentication purposes. On the other hand, obtained equal error rate (EER) values for signals recorded during the same session range from 1.0% for high-quality signals recorded in controlled conditions to 8% for those recorded in conditions closer to real-world scenarios. Moreover, in certain scenarios, EER values rise up to 23.2% for signals recorded over different days, signaling that performance degradation could take place with time.