Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations—(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.
Ambulatory mental stress monitoring requires longterm physiological measurements. This paper presents a data collection protocol for ambulatory recording of physiological parameters for stress measurement purposes. We present a wearable sensor system for ambulatory recording of ECG, EMG, respiration and skin conductance. The system also records various context parameters: acceleration, temperature and relative humidity. We show that the sensor system is capable of long-term, noninvasive, nonobtrusive, wireless physiological monitoring. We also show some preliminary results of a stress estimation method. These results reveal already a number of context-related issues we will have to take into account in future work. The presented sensor system enables physiological and context data collection and further development of personalized real-time stress detection algorithms.
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