Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Comparative of ballistocardiogram processing methods based on fiber specklegram sensors

The ballistocardiogram (BCG) is a graphic representation of the movements of the body associated with cardiac activity. In this article, a 10-min BCG has been captured for ten different volunteers with a polymer optical fiber (POF) specklegram sensor. This transducer, which is composed of a charge-coupled device (CCD) camera, a laser emitting diode, and two meters of POF, allows capturing the BCG by analyzing how the induced speckle pattern changes over time. These changes are related to cardiac activity. Several processing methods have been compared to determine which method achieves the best performance: complex cepstrum, power of spectral density (PSD), Pam-Tompkins algorithm, wavelet, autocorrelation, Savitzky?Golay filter, mean absolute deviation, and Hilbert transform. Accuracy and resource consumption have been characterized and compared for these methods. Hilbert, PSD, and Savitzky-Golay exhibit both small errors and computational costs. This article describes a baseline for the main frequency determination of POF-based BCG signals in real-time.This work was supported by the Project PID2019-107270RB-C21 through MCIN/ AEI /10.13039/501100011033

Classification of health deterioration by geometric invariants

The authors are grateful to the Operational Programme "Development of the Internal Grant Agency of the University of Hradec Kralove", reg. no. CZ.02.2.69/0.0/0.0/19_073/0016949, project no. IGRA-TYM-2021008 (investigators: Damian Busovsky and Katerina Voglova) .This study was also possible thanks to the project TP01010032 "The Centre of Creative Activities and Knowledge Transfer at University Hradec Kralove." This project was co -financed by the state budget of the Technology Agency of the Czech Republic under the GAMA 2 Progamme.Furthermore, the authors are grateful to the Excellence project PrF UHK 2215/2023-2024 for its financial support.Background and Objectives: Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient's medical state requires devices placed on the body, which may cause discomfort. Our approach is based on the processing of long-term ballistocardiography data, which were measured using a sensory pad placed under the patient's mattress.Methods: The investigated dataset was obtained via long-term measurements in retirement homes and intensive care units (ICU). Data were measured unobtrusively using a measuring pad equipped with piezoceramic sensors. The proposed approach focused on the processing methods of the measured ballistocardiographic signals, Cartan curvature (CC), and Euclidean arc length (EAL).Results: For analysis, 218,979 normal and 216,259 aberrant 2-second samples were collected and classified using a convolutional neural network. Experiments using cross-validation with expert threshold and data length revealed the accuracy, sensitivity, and specificity of the proposed method to be 86.51Conclusions: The proposed method provides a unique approach for an early detection of health concerns in an unobtrusive manner. In addition, the suitability of EAL over the CC was determined.Operational Programme "Development of the Internal Grant Agency of the University of Hradec Kralove" CZ.02.2.69/0.0/0.0/19_073/0016949, IGRA-TYM-2021008Centre of Creative Activities and Knowledge Transfer at Uni- versity Hradec KraloveState budget of the Technology Agency of the Czech RepublicCentre of Creative Activities and Knowledge Transfer at University Hradec KraloveExcellence project PrF UHKTP01010032, 2215/2023-202

Performance Comparison for Ballistocardiogram Peak Detection Methods

Citation: Suliman, A., Carlson, C., Ade, C. J., Warren, S., & Thompson, D. E. (2019). Performance Comparison for Ballistocardiogram Peak Detection Methods. IEEE Access, 7, 53945–53955. https://doi.org/10.1109/ACCESS.2019.2912650A number of research groups have proposed methods for ballistocardiogram (BCG) peak detection toward the identification of individual cardiac cycles. However, objective comparisons of these proposed methods are lacking. This paper, therefore, conducts a systematic and objective performance evaluation and comparison of several of these approaches. Five peak-detection methods (three replicated from the literature and two adapted from code provided by the methods' authors) are compared using data from 30 volunteers. A basic cross-correlation approach was also included as a sixth method. Two high-performing methods were identified: the method proposed by Sadek et al. and the method proposed by Brüser et al. The first achieved the highest average peak-detection rate of 94%, the lowest average false alarm rate of 0.0552 false alarms per second, and a relatively small mean absolute error between the real and detected peaks: 0.0175 seconds. The second method achieved the lowest mean absolute error of 0.0088 seconds between the real and detected peaks, an average peak-detection success rate of 89%, and 0.0766 false alarms per second. All metrics are averaged across participants

Digital Optical Ballistocardiographic System for Activity, Heart Rate, and Breath Rate Determination during Sleep

In this work, we present a ballistocardiographic (BCG) system for the determination of heart and breath rates and activity of a user lying in bed. Our primary goal was to simplify the analog and digital processing usually required in these kinds of systems while retaining high performance. A novel sensing approach is proposed consisting of a white LED facing a digital light detector. This detector provides precise measurements of the variations of the light intensity of the incident light due to the vibrations of the bed produced by the subject’s breathing, heartbeat, or activity. Four small springs, acting as a bandpass filter, connect the boards where the LED and the detector are mounted. Owing to the mechanical bandpass filtering caused by the compressed springs, the proposed system generates a BCG signal that reflects the main frequencies of the heartbeat, breathing, and movement of the lying subject. Without requiring any analog signal processing, this device continuously transmits the measurements to a microcontroller through a twowire communication protocol, where they are processed to provide an estimation of the parameters of interest in configurable time intervals. The final information of interest is wirelessly sent to the user’s smartphone by means of a Bluetooth connection. For evaluation purposes, the proposed system has been compared with typical BCG systems showing excellent performance for different subject positions. Moreover, applied postprocessing methods have shown good behavior for information separation from a single-channel signal. Therefore, the determination of the heart rate, breathing rate, and activity of the patient is achieved through a highly simplified signal processing without any need for analog signal conditioning.Junta de Andalucia European Commission PYC20-RE-040 UGR MCIN/AEI/10.13039/501100011033/with PID2019-103938RB-I00European Commissio

Huomaamattomat mittausmenetelmät unen laadun tarkkailussa

Sleep is an important part of health and well-being. While sleep quantity is directly measurable, sleep quality has traditionally been assessed with subjective methods such as questionnaires. The study of sleep disorders has for a long time been confined to clinical environments, and patients have had to endure cumbersome procedures involving multiple electrodes placed on the body. Recent developments in sensor technology as well as data analysis methods have enabled continuous, unobtrusive sleep data recording in the home environment. This has opened new possibilities for studying various sleep parameters and their effect on the quality of sleep. This thesis consists of two parts. The first part is a literature review examining the field of sleep quality research with focus on the application of intelligent methods and signal processing. The second part is a descriptive data analysis look at sleep data obtained with non-invasive sensors.Uni on terveyden ja hyvinvoinnin keskeinen tekijä. Unen määrä on helposti mitattavissa, mutta unen laatua on perinteisesti seurattu kyselylomakkeiden kaltaisin subjektiivisin menetelmin. Unihäiriöiden tutkiminen on pitkään rajoittunut kliinisiin ympäristöihin, ja potilaiden on täytynyt sietää hankalia tutkimusmenetelmiä useine kehoon kiinnitettävine elektrodeineen. Anturiteknologian ja data-analyysimenetelmien kehittyminen on mahdollistanut unidatan jatkuvan ja huomaamattoman tallentamisen kotiympäristössä. Tämä on avannut uusia mahdollisuuksia sekä unen ominaisuuksien että niiden unen laatuun vaikuttavien tekijöiden tutkimiselle. Tämä tutkimus jakautuu kahteen osaan. Ensimmäinen osa on kirjallisuuskatsaus unen laadun tutkimukseen, painopisteenä älykkäiden menetelmien ja signaalinkäsittelyn soveltaminen. Toisessa osassa esitellään huomaamattomilla sensoreilla kerättävän unidatan tutkimista ja sen deskriptiivistä data-analyysiä, esimerkkinä ballistokardiografia

Development of a Portable Seat Cushion for the Estimation of Heart Rate Using Ballistocardiography

Cardiovascular diseases are a leading contributor of health problems all over the world and are the second leading cause of death. They are also the cause of significant economic burden, costing billions of dollars in healthcare every year. With an aging population, the strain on the healthcare system, both in terms of costs and care provision, is expected to worsen. Frequent cardiac assessment can provide essential information towards diagnosis, monitoring, and treatment, which can mitigate symptoms and improve health outcomes for people with conditions such as heart failure. This has led to increasing interest in cardiac assessment at home. Additionally, for some populations like people with limited mobility and older adults, long term vitals monitoring at a clinical setting is not feasible, making at-home monitoring more viable and economical. Most devices available for cardiac monitoring at home are wearables. While wearable technology can be accurate, it requires compliance and maintenance, which is not an ideal solution for all populations. For example, people who are not comfortable using wearables or people with a cognitive impairment may not want or be able to use wearables, which could exclude these user types from at home monitoring. Keeping these factors under consideration, the past decade has seen an increased interest in the development of technologies for Ambient Assisted Living (i.e., smart technologies integrated into a user's environment). These technologies have the potential for ongoing health monitoring in an unobtrusive manner. This thesis presents research into the development of a smart seat cushion for heart rate monitoring. The cushion is able to calculate the heart rate of a person seated on it by acquiring their Ballistocardiogram (BCG). BCG is a cardiovascular signal corresponding to the displacement of the body in response to the heart pumping blood at every heartbeat. The prototype seat cushion has load cells embedded inside it that sense the micromovements of the body and translate it to an electrical signal. An analog signal conditioning circuit amplifies and filters this signal to enhance the components corresponding to BCG before it is converted to digital form. A pilot study was conducted with twenty participants to acquire BCG in real-world scenarios: 1) sitting still, 2) reading, 3) using a computer, 4) watching TV, and 5) having a conversation. Heart rate was calculated using a novel algorithm based on Continuous Wavelet Transform by detecting the largest peaks (referred to as the J-peaks) in the BCG. Excluding three outliers, the algorithm is able to achieve an overall accuracy of 94.6% compared to gold standard Electrocardiography (ECG). This accuracy is observed to be as good as or better than those of existing wearable heart rate monitors. The seat cushion developed in this thesis research can serve as a portable solution for cardiac monitoring and can integrate into an ambient health monitoring system, offering continued monitoring of heart rate while requiring no perceived effort to operate it. Future work includes exploring different sensor configurations, machine learning based approaches for improving J-peaks detection, and real-time monitoring of heart rate