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Milena Č, Romano C, De Tommasi F, Carassiti M, Formica D, Schena E, Massaroni C. Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031615. [PMID: 36772656 PMCID: PMC9920051 DOI: 10.3390/s23031615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/02/2023] [Accepted: 01/28/2023] [Indexed: 05/26/2023]
Abstract
Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart's electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body's surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.
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Affiliation(s)
- Čukić Milena
- Empa Materials Science and Technology, Biomimetic Membranes and Textiles, 9014 St. Gallen, Switzerland
- 3EGA B.V., 1062 KS Amsterdam, The Netherlands
| | - Chiara Romano
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Francesca De Tommasi
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
- Unit of Anesthesia, Intensive Care and Pain Management, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Massimiliano Carassiti
- Unit of Anesthesia, Intensive Care and Pain Management, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Domenico Formica
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
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Santucci F, Lo Presti D, Massaroni C, Schena E, Setola R. Precordial Vibrations: A Review of Wearable Systems, Signal Processing Techniques, and Main Applications. SENSORS 2022; 22:s22155805. [PMID: 35957358 PMCID: PMC9370957 DOI: 10.3390/s22155805] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/21/2022] [Accepted: 07/28/2022] [Indexed: 02/06/2023]
Abstract
Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects.
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Affiliation(s)
- Francesca Santucci
- Unit of Automatic Control, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy;
- Correspondence: ; Tel.: +39-062-2541-9603
| | - Daniela Lo Presti
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy; (D.L.P.); (C.M.); (E.S.)
| | - Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy; (D.L.P.); (C.M.); (E.S.)
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy; (D.L.P.); (C.M.); (E.S.)
| | - Roberto Setola
- Unit of Automatic Control, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy;
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Wajdan A, Jahren TS, Villegas-Martinez M, Khan FH, Halvorsen PS, Odland HH, Elle OJ, Solberg AHS, Remme EW. Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer. IEEE J Biomed Health Inform 2022; 26:4450-4461. [PMID: 35679388 DOI: 10.1109/jbhi.2022.3181148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
BACKGROUND Miniaturized accelerometers incorporated in pacing leads attached to the myocardium, are used to monitor cardiac function. For this purpose functional indices must be extracted from the acceleration signal. A method that automatically detects time of aortic valve opening (AVO) and aortic valve closure (AVC) will be helpful for such extraction. We tested if deep learning can be used to detect these valve events from epicardially attached accelerometers, using high fidelity pressure measurements to establish ground truth for these valve events. METHOD A deep neural network consisting of a CNN, an RNN, and a multi-head attention module was trained and tested on 130 recordings from 19 canines and 159 recordings from 27 porcines covering different interventions. Due to limited data, nested cross-validation was used to assess the accuracy of the method. RESULT The correct detection rates were 98.9% and 97.1% for AVO and AVC in canines and 98.2% and 96.7% in porcines when defining a correct detection as a prediction closer than 40 ms to the ground truth. The incorrect detection rates were 0.7% and 2.3% for AVO and AVC in canines and 1.1% and 2.3% in porcines. The mean absolute error between correct detections and their ground truth was 8.4 ms and 7.2 ms for AVO and AVC in canines, and 8.9 ms and 10.1 ms in porcines. CONCLUSION Deep neural networks can be used on signals from epicardially attached accelerometers for robust and accurate detection of the opening and closing of the aortic valve.
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Xia Z, Shandhi MMH, Li Y, Inan OT, Zhang Y. The Delineation of Fiducial Points for Non-Contact Radar Seismocardiogram Signals Without Concurrent ECG. IEEE J Biomed Health Inform 2021; 25:1031-1040. [PMID: 32750965 DOI: 10.1109/jbhi.2020.3009997] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Non-contact sensing of seismocardiogram (SCG) signals through a microwave Doppler radar is promising for biomedical applications. However, the delineation of fiducial points for radar SCG still relies on concurrent ECG which requires a contact sensor and limits the complete non-contact detection of SCG. METHODS Instead of ECG, a new reference signal, the radar displacement signal of heartbeat (RDH), was derived through the complex Fourier transform and the band pass filtering of the radar signal. The RDH signal was used to locate each cardiac cycle and mask the systolic profile, which was further used to detect an important fiducial point, aortic valve opening (AO). The beat-to-beat interval was estimated from AO-AO interval and compared with the gold standard, ECG R-to-R interval. RESULTS For the 22 subjects in the study, the evaluation of the AOs detected by RDH (AORDH) shows the average detection ratio can reach 90%, indicating a high ratio of the AORDH that are exactly the same as AO detected using the ECG R-wave (AOECG). Additionally, the left ventricular ejection time (LVET) values estimated from the ensemble averaged radar waveform through AORDH segmentation are within 2 ms of those through AOECG segmentation, for all the detected subjects. Further analysis demonstrates that the beat-to-beat intervals calculated from AORDH have an average root-mean-square-deviation (RMSD) of 53.73 ms when compared with ECG R-to-R intervals, and have an average RMSD of 23.47 ms after removing the beats in which AO cannot be identified. CONCLUSIONS Radar signal RDH can be used as a reference signal to delineate fiducial points for non-contact radar SCG signals. SIGNIFICANCE This study can be applied to develop complete non-contact sensing of SCG and monitoring of vital signs, where contact-based SCG is not feasible.
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Abstract
Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.
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Affiliation(s)
- Amirtahà Taebi
- Department of Biomedical Engineering, University of California Davis, One Shields Ave, Davis, CA 95616, USA
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
- Correspondence: ; Tel.: +1-407-580-4654
| | - Brian E. Solar
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
| | - Andrew J. Bomar
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
- College of Medicine, University of Central Florida, 6850 Lake Nona Blvd, Orlando, FL 32827, USA
| | - Richard H. Sandler
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
- College of Medicine, University of Central Florida, 6850 Lake Nona Blvd, Orlando, FL 32827, USA
| | - Hansen A. Mansy
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
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Choudhary T, Sharma LN, Bhuyan MK. Automatic Detection of Aortic Valve Opening Using Seismocardiography in Healthy Individuals. IEEE J Biomed Health Inform 2018; 23:1032-1040. [PMID: 29993702 DOI: 10.1109/jbhi.2018.2829608] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Accurate detection of fiducial points in a seismocardiogram (SCG) is a challenging research problem for its clinical application. In this paper, an automated method for detecting aortic valve opening (AO) instants using the dorso-ventral component of the SCG signal is proposed. This method does not require electrocardiogram (ECG) as a reference signal. After preprocessing the SCG, multiscale wavelet decomposition is carried out to get signal components in different wavelet subbands. The subbands having possible AO peaks are selected by a newly proposed dominant-multiscale-kurtosis- and dominant-multiscale-central-frequency-based criterion. The signal is reconstructed using selected subbands, and it is emphasized using the weights derived from the proposed relative squared dominant multiscale kurtosis. The Shannon energy followed by autocorrelation coefficients is computed for systole envelope construction. Finally, AO peaks are detected by a Gaussian-derivative-filtering-based scheme. The robustness of the proposed method is tested using clean and noisy SCG signals from the combined measurement of ECG, breathing, and SCG database. Evaluation results show that the method can achieve an average sensitivity of 94%, a prediction rate of 90%, and a detection accuracy of 86% approximately over 4585 analyzed beats.
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