Related Experiment Video
Updated: Aug 11, 2025
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort
Shen Feng1,2, Xianda Wu1,2, Andong Bao1,2
1Department of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, China.
Insights
This study introduces a machine learning approach for in-home heart failure detection using ballistocardiography (BCG) and respiratory signals. The method achieved high accuracy, improving non-contact diagnosis for cardiovascular disease.
Area of Science:
- Biomedical Engineering
- Cardiovascular Health
- Artificial Intelligence in Medicine
Background:
- The rise of COVID-19 and healthcare costs has increased demand for remote cardiovascular disease monitoring.
- Ballistocardiography (BCG) offers non-contact detection of cardiac activity and heart failure (HF).
- Current HF diagnosis often requires inconvenient ECG-aided quality assessment.
Purpose of the Study:
- To develop a machine learning-aided scheme for in-home heart failure detection.
- To utilize BCG signals and respiratory effort signals for enhanced HF detection without requiring heartbeat localization.
- To leverage the interconnectedness of heart and lung systems for improved diagnostic features.
Main Methods:
- Recorded vital sign sequences, including BCG and respiratory effort, using a piezoelectric sensor.
- Extracted linear and non-linear features from both BCG and respiratory effort signals.
- Validated the scheme using Leave-One-Out (LOO) and Leave-One-Subject-Out (LOSO) cross-validation with various machine learning classifiers.
Main Results:
- The machine learning scheme demonstrated robust performance across four classifiers.
- Achieved high accuracy rates of 94.97% (LOO) and 87.00% (LOSO) in heart failure detection.
- Confirmed that respiratory and cardiopulmonary features significantly benefit heart failure detection (LVEF).
Conclusions:
- A novel machine learning-aided diagnostic scheme for heart failure was proposed.
- The scheme effectively utilizes the heart-lung system relationship for improved in-home HF detection.
- The integrated use of BCG, respiratory, and cardiopulmonary features enhances non-contact cardiovascular disease monitoring.
Abstract:
Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ). Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.
