title: Robust cardiac event change detection method for long-term healthcare monitoring applications.
abstract: A long-term continuous cardiac health monitoring system highly demands more battery power for real-time transmission of electrocardiogram (ECG) signals and increases bandwidth, treatment costs and traffic load of the diagnostic server. In this Letter, the authors present an automated low-complexity robust cardiac event change detection (CECD) method that can continuously detect specific changes in PQRST morphological patterns and heart rhythms and then enable transmission/storing of the recorded ECG signals. The proposed CECD method consists of four stages: ECG signal quality assessment, R-peak detection and beat waveform extraction, temporal and RR interval feature extraction and cardiac event change decision. The proposed method is tested and validated using both normal and abnormal ECG signals including different types of arrhythmia beats, heart rates and signal quality. Results show that the method achieves an average sensitivity of 99.76%, positive predictivity of 94.58% and overall accuracy of 94.32% in determining the changes in heartbeat waveforms of the ECG signals.
authors.affiliation: School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha-751013 , India.
authors.forename: Udit
authors.initials: U
authors.lastname: Satija
authors.affiliation: School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha-751013 , India.
authors.forename: Barathram
authors.initials: B
authors.lastname: Ramkumar
authors.affiliation: School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha-751013 , India.
authors.forename: M Sabarimalai
authors.initials: MS
authors.lastname: Manikandan
keywords: CECD method; ECG signals; PQRST morphological patterns; R-peak detection; RR interval feature extraction; automated low-complexity robust cardiac event change detection; battery power; beat waveform extraction; cardiac event change decision; continuous cardiac health monitoring system; electrocardiogram; electrocardiography; feature extraction; heart rhythms; heartbeat waveforms; long-term healthcare monitoring applications; medical signal processing; patient monitoring; robust cardiac event change detection method; temporal feature extraction; waveform analysis