Heart Rate Estimation from Incomplete Electrocardiography Signals.

Area of Science:

• Physiological monitoring
• Biomedical signal processing
• Machine learning applications

Background:

• Heart rate (HR) is a key physiological indicator.
• Estimating HR from incomplete electrocardiography (ECG) signals presents challenges.
• Existing methods often struggle with time series missing patterns.

Purpose of the Study:

• To develop and evaluate a deep learning approach for short-time HR estimation from ECG with missing data.
• To recover complete heartbeat signals from segments shorter than one cardiac cycle.
• To compare the performance of Bidirectional Long Short-Term Memory (Bi-LSTM) and Temporal Convolutional Network (TCN) models.

Main Methods:

• Utilized deep learning models: Bi-LSTM and TCN.
• Implemented signal recovery for ECG segments with durations less than one cardiac cycle.
• Estimated HR by combining input and predicted output from recovered segments.
• Validated models on the PhysioNet dataset, including normal and arrhythmia databases.

Main Results:

• Both Bi-LSTM and TCN demonstrated effective HR estimation from incomplete ECG signals.
• Accurate results were achieved in normal heartbeat datasets (gamma > 0.7, RMSE < 10).
• Reliable estimations were obtained even in arrhythmia databases (gamma > 0.6, RMSE < 30).

Conclusions:

• Deep learning models can reliably estimate HR from incomplete ECG data.
• The proposed method offers a viable approach for physiological monitoring in time-constrained scenarios.
• This technique provides valuable insights for time series analysis with missing data patterns.