Moving Auto-Correlation Window Approach for Heart Rate Estimation in Ballistocardiography Extracted by Mattress-Integrated Accelerometers

Abstract

Continuous heart monitoring is essential for early detection and diagnosis of cardiovascular diseases, which are key factors for the evaluation of health status in the general population. Therefore, in the future, it will be increasingly important to develop unobtrusive and transparent cardiac monitoring technologies for the population. The possible approaches are the development of wearable technologies or the integration of sensors in daily-life objects. We developed a smart bed for monitoring cardiorespiratory functions during the night or in the case of continuous monitoring of bedridden patients. The mattress includes three accelerometers for the estimation of the ballistocardiogram (BCG). BCG signal is generated due to the vibrational activity of the body in response to the cardiac ejection of blood. BCG is a promising technique but is usually replaced by electrocardiogram due to the difficulty involved in detecting and processing the BCG signals. In this work, we describe a new algorithm for heart parameter extraction from the BCG signal, based on a moving auto-correlation sliding-window. We tested our method on a group of volunteers with the simultaneous co-registration of electrocardiogram (ECG) using a single-lead configuration. Comparisons with ECG reference signals indicated that the algorithm performed satisfactorily. The results presented demonstrate that valuable cardiac information can be obtained from the BCG signal extracted by low cost sensors integrated in the mattress. Thus, a continuous unobtrusive heart-monitoring through a smart bed is now feasible.

The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care

“…Part a shows a BCGbased chair that using cuffless blood pressure measurement methods that detect the peak pressure via signals measured using photoplethysmogram (PPG) and electrocardiogram (ECG) sensors the pulse transit time (PTT) or pulse wave velocity (PWV) have been studied [30]. Part b shows a BCG-based mattress which had three accelerometers to measure the ballistocardiogram (BCG) [31]. Part c shows an ECG-based weighing scale which measures using dry electrodes R-J intervals were extracted as a BP correlated parameter at every cardiac cycle [32].…”

“…Part b shows a BCG-based mattress which had three accelerometers to measure the ballistocardiogram (BCG) [31]. Part c shows an ECG-based weighing scale which measures using dry electrodes R-J intervals were extracted as a BP correlated parameter at every cardiac cycle [32].…”

Attitude and Needs Toward MTM Applications of Chronic Disease in China: A Questionnaire Survey

“…It is worth noting that chronic diseases in China in 2019 accounted for 88.5% of the total deaths in the "Report on Nutrition and Chronic Diseases of Chinese Residents" (2). The number of chronic disease patients is increasing, which seriously threatens the health of residents and becomes a major public health issue affecting economic and social development (3). Characterized by high morbidity, long-term medication, and complex types of medication, chronic diseases lead to medication misunderstanding and poor medication adherence in most patients, which is closely linked to increased morbidity, mortality, and medical costs, making the long-term treatment of chronic diseases a global concern (4-7).…”

Textile-Based Pressure Sensing Matrix for In-Bed Monitoring of Subject Sleeping Posture and Breathing Activity

“…In our previous work reported in [5], we demonstrated the capability of the smart bed to fuse all sensor data (pressure matrix, accelerometers, and environmental sensors) to perform automatic classification of sleep quality with performance comparable to standard polysomnography. In addition, in [7], we showed the methods and results related to extraction of the heart rate through ballistocardiography applied on the signals of the set of accelerometers integrated in the smart bed. In this work, we focus on the pressure mapping layer designed as a two-dimensional array of 195 textile pressure sensors capable of detecting the distribution of pressure exerted by the subject sleeping on the mattress.…”

“…The signals from the pressure sensors are processed to classify the posture and movements of the subjects and to extract respiratory activity. The classification of posture and movement are the basis for sleep analysis described in [7] and could be also employed to give context to the acquisition of physiological signals (e.g., compensation of artifacts and optimal sensor selection). The respiratory signal in addition to the other sensors of the smart bed such as accelerometers and microphones could be exploited as part of an unobtrusive physiological acquisition system capable of detecting cardiopulmonary activity (including sleep apnea) and relevant respiratory symptoms such as cough and wheezing.…”