Key result
Compressing ECG biosignals prior to transmission reduces computational and storage burdens while amplifying main features to produce higher heartbeat detection accuracy.
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Using optimized compression algorithms for remote ECG monitoring can improve diagnostic efficiency and performance without the need for exhaustive computational resources.
In the digital medicine field, biosignals, such as those of an electrocardiogram (ECG), are collected regularly for screening and diagnosis, and there continues to be an increasingly substantial shift towards collecting long-term ECG signals for remote monitoring, e.g., in smart homes. ECG signal collection is quite simple and only requires the use of inexpensive sensors, an active Internet connection, and a mobile device that acts as the medium between the sensors and the Internet (e.g., a mobile phone or laptop). Despite the ease and convenience of remote ECG data collection and transmission, the amount of time and energy required for the related remote computational processes remains a major limitation. This short note discusses a biosignal approach that uses fewer biomedical data for screening and diagnosis that is, compared to current data collection methods, equally, if not more, efficient.
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Mohamed Elgendi (2018) studied Remote ECG monitoring and biosignal analysis. ECG data compression approach vs. Conventional uncompressed ECG data collection was evaluated. Compressing ECG biosignals prior to transmission reduces computational and storage burdens while amplifying main features to produce higher heartbeat detection accuracy.
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