Key result
An algorithm using Continuous Wavelet Transform and J48 with a commercial pulsimeter and SCR successfully detected heart rate (99.42%), arrhythmia (93.48%), extrasystoles (99.29%), and stress (94.02%).
An algorithm using CWT and J48 classifiers with a commercial pulsimeter and SCR can accurately detect cardiac alterations and stress levels, potentially useful for telemedicine and during physical activity.
May aid wearable monitoring in telemedicine; leaves open prospective clinical validation before practice adoption.
This paper presents the results of using a commercial pulsimeter as an electrocardiogram (ECG) for wireless detection of cardiac alterations and stress levels for home control. For these purposes, signal processing techniques (Continuous Wavelet Transform (CWT) and J48) have been used, respectively. The designed algorithm analyses the ECG signal and is able to detect the heart rate (99.42%), arrhythmia (93.48%) and extrasystoles (99.29%). The detection of stress level is complemented with Skin Conductance Response (SCR), whose success is 94.02%. The heart rate variability does not show added value to the stress detection in this case. With this pulsimeter, it is possible to prevent and detect anomalies for a non-intrusive way associated to a telemedicine system. It is also possible to use it during physical activity due to the fact the CWT minimizes the motion artifacts.
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Villarejo et al. (2013) studied Cardiac alterations and stress. Algorithm based on CWT and J48 using a commercial pulsimeter and SCR was evaluated on Detection of heart rate, arrhythmia, extrasystoles, and stress level. An algorithm using Continuous Wavelet Transform and J48 with a commercial pulsimeter and SCR successfully detected heart rate (99.42%), arrhythmia (93.48%), extrasystoles (99.29%), and stress (94.02%).
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