Key result
The proposed real-time ECG signal processing algorithm using a mobile phone achieved a sensitivity of 96.97% and a positive predictivity of 95.63% for QRS detection.
Why the study?
Does a mobile phone-based real-time ECG signal processing system accurately detect cardiac arrhythmias in ambulatory patients?
Does a mobile phone-based real-time ECG signal processing system accurately detect cardiac arrhythmias in ambulatory patients?
A mobile phone-based ECG processing algorithm using Hilbert transform demonstrated high sensitivity and positive predictivity for detecting cardiac arrhythmias.
Answers are grounded in this paper and cite their sources.
May support mobile ECG QRS detection; leaves open prospective validation for arrhythmia screening in patients.
The real-time ECG signal processing system based on mobile phones is very effective in identifying continuous ambulatory patients. It could monitor cardiovascular patients in their daily life and warns them in case of cardiac arrhythmia. An ECG signal of a patient is processed by a mobile phone with this proposed algorithm. An IIR low-pass filter is used to remove the noise and it has the 55 Hz cutoff frequency and order 3. The obtained SNR showed a desirable noise removal and it helps physicians in their diagnosis. In this paper, Hilbert transform was used and the R peaks are important component to differ normal beats from abnormal ones. The results of sensitivity and positive predictivity of algorithm are 96.97% and 95.63% respectively. If an arrhythmia occurred, 4 seconds of this signal is displayed on the mobile phone then it will be sent to a remote medical center by TCP/IP protocol.
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Raeiatibanadkooki et al. (2014) studied Cardiac arrhythmia (n=73). Mobile phone-based real-time ECG signal processing algorithm (IIR filter and Hilbert transform) vs. Other QRS detection algorithms was evaluated on Sensitivity and positive predictivity for QRS detection. The proposed real-time ECG signal processing algorithm using a mobile phone achieved a sensitivity of 96.97% and a positive predictivity of 95.63% for QRS detection.
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