Detection and Analysis of Heartbeats in Seismocardiogram Signals

Mora, Niccolò; Cocconcelli, Federico; Matrella, Guido; Ciampolini, Paolo

doi:10.3390/s20061670

Cited by 21 publications

(13 citation statements)

References 39 publications

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“…Finally, all 5 additional minutes of data were recorded from the subjects after the music ended. SCG [21,22] Daily Life Activities…”

Section: Methods S E I S M O C a R D I O G R A P H Y A C T I V I T Y A N A L Y S I S P O S T U R E A N A L Y S I S L O C A L I Z A T I O mentioning

confidence: 99%

“…As a next step, analysis of Heart Rate (HR) [20] and Heart Rate Variability (HRV) [16] is reported in the literature. Estimation of Inter-beat Interval (IBI) [21], Aortic valve Opening (AO)-peaks [22], Pre-Ejection Period (PEP) [23] and Left Ventricular Ejection Time (LVET) [24] ands identification of some heart anomalies [25,26] are also conducted via chest-worn IMUs.…”

Section: Seismocardiographymentioning

confidence: 99%

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Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Rahmani

Berkvens

Weyn

2021

Sensors

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Inertial Measurement Units (IMUs) are frequently implemented in wearable devices. Thanks to advances in signal processing and machine learning, applications of IMUs are not limited to those explicitly addressing body movements such as Activity Recognition (AR). On the other hand, wearing IMUs on the chest offers a few advantages over other body positions. AR and posture analysis, cardiopulmonary parameters estimation, voice and swallowing activity detection and other measurements can be approached through chest-worn inertial sensors. This survey tries to introduce the applications that come with the chest-worn IMUs and summarizes the existing methods, current challenges and future directions associated with them. In this regard, this paper references a total number of 57 relevant studies from the last 10 years and categorizes them into seven application areas. We discuss the inertial sensors used as well as their placement on the body and their associated validation methods based on the application categories. Our investigations show meaningful correlations among the studies within the same application categories. Then, we investigate the data processing architectures of the studies from the hardware point of view, indicating a lack of effort on handling the main processing through on-body units. Finally, we propose combining the discussed applications in a single platform, finding robust ways for artifact cancellation, and planning optimized sensing/processing architectures for them, to be taken more seriously in future research.

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“…Finally, all 5 additional minutes of data were recorded from the subjects after the music ended. SCG [21,22] Daily Life Activities…”

Section: Methods S E I S M O C a R D I O G R A P H Y A C T I V I T Y A N A L Y S I S P O S T U R E A N A L Y S I S L O C A L I Z A T I O mentioning

confidence: 99%

Section: Seismocardiographymentioning

confidence: 99%

Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Rahmani

Berkvens

Weyn

2021

Sensors

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“…A different approach is set by using bed-based BCG systems, when the signal is measured in combination of transversal and dorsoventral axes. Then, the z-axis is given by the dorsoventral one [ 66 , 67 , 68 ].…”

Section: Overview Of Already Existing Methodsmentioning

confidence: 99%

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

Šidiková

Martínek

Kawala-Sterniuk

et al. 2020

Sensors

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This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.

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“…The process was divided into two subtasks: first, candidate beats were identified and validated, and next, timing annotation was done. In [115], a Convolutional Variational AutoEncoder based unsupervised method was proposed for extracting the heartbeat complexes and the associated morphological informations. In [116], a SCG-based approach was proposed using wavelet decomposition, Fourier-based envelope detection, and time-averaged power spectral density for the estimation of HR.…”

Section: Heart Rate and Heart Rate Variability Indexmentioning

confidence: 99%

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

et al. 2021

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In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included.

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Detection and Analysis of Heartbeats in Seismocardiogram Signals

Cited by 21 publications

References 39 publications

Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

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