A unified methodology for heartbeats detection in seismocardiogram and ballistocardiogram signals

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets (p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found (p < 0.01) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors

Detection and analysis of heartbeats in seismocardiogram signals

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space)

A Hidden Markov Model for Seismocardiography

This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record.We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and 9 [ms], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services

Artifact Noise Removal Techniques and Automatic Annotation on Seismocardiogram Using Two Tri-axial Accelerometers

Heart disease are ones of the most death causes in the world. Many studies investigated in evaluating the heart performance in order to detect cardiac diseases in the early stage. The aim of this study is to monitor the heart activities in long-term on active people to reduce the risk of heart disease. Specifically, this study investigates the motion noise removal techniques using two-accelerometer sensor system and various positions of the sensors on gentle movement and walking of subjects. The study also ends up with algorithms to detect cardiac phases and events on Seismocardiogram (SCG) based on acceleration sensors. A Wi-Fi based data acquisition system and a framework on Matlab are developed to collect and process data while the subjects are in motion. The tests include eight volunteers who have no record of heart disease. The walking and running data on the subjects are analyzed to find the minimal-noise bandwidth of the SCG signal. This bandwidth is used to design bandpass filters in the motion noise removal techniques and peak signal detection. There are three main techniques of combining data of the two sensors to mitigate the motion artifact: analog processing, digital processing and fusion processing. The analog processing comprises analog ADDER/SUBTRACTOR and bandpass filter to remove the motion before entering the data acquisition system. The digital processing processes all the data using combinations of total acceleration and z-axis only acceleration. The fusion processing automatically controls the amplification gain of the SUBTRACTOR to improve signal quality as long as a signal saturation is detected. The three techniques are tested on three placements of sensors including horizontal, vertical, and diagonal on gentle motion and walking. In general, the total acceleration and z-axis acceleration are best techniques to deal with gentle motion on all placements which improve average systolic signal-noise-ratio (SNR) around 2 times and average diastolic SNR around 3 times comparing to only one accelerometer. With walking motion, overall the ADDER and zaxis acceleration are best techniques on all placements of the sensors on the body which enhance about 7 times of average systolic SNR and about 11 times of average diastolic SNR comparing to only one accelerometer. The combination of two sensors also increases the average number of recognizable systole and diastole on walking corresponding to 71.3 % and 43.8 % comparing toiii only one sensor. Among the sensor placements, the performance of horizontal placement of the sensors is outstanding comparing with other positions on all motions. There are two detection stages to detect events in the SCG for automatic annotation. First, two algorithms including moving average threshold and interpolation are applied to locate the systolic and diastolic phases. Then, based on those identified phases, cardiac events are found in the searched intervals using two outstanding characteristics of the SCG. The two algorithms of phase detection are examined on the stationary data sets of digital processing and horizontal placement. The total acceleration of only one sensor is also calculated for comparison. With moving average threshold algorithm, the average error and missing rates of total acceleration and z-axis acceleration are 1.8 % and 2.1 % respectively which are lower than using one accelerometer (3.6 %). With interpolation algorithm, the average error and missing rates of total acceleration and z-axis acceleration are in the order of 2.3 % and 2.4 % which are still lower than one accelerometer. The average calculation time of the moving average algorithm is lower than the interpolation counterpart. The real-time mode of detection algorithms is also demonstrated on Matlab framework to prove the possibility of practical applications

ELECTRO-MECHANICAL DATA FUSION FOR HEART HEALTH MONITORING

Heart disease is a major public health problem and one of the leading causes of death worldwide. Therefore, cardiac monitoring is of great importance for the early detection and prevention of adverse conditions. Recently, there has been extensive research interest in long-term, continuous, and non-invasive cardiac monitoring using wearable technology. Here we introduce a wearable device for monitoring heart health. This prototype consists of three sensors to monitor electrocardiogram (ECG), phonocardiogram (PCG), and seismocardiogram (SCG) signals, integrated with a microcontroller module with Bluetooth wireless connectivity. We also created a custom printed circuit board (PCB) to integrate all the sensors into a compact design. Then, flexible housing for the electronic components was 3D printed using thermoplastic polyurethane (TPU). In addition, we developed peak detection algorithms and filtering programs to analyze the recorded cardiac signals. Our preliminary results show that the device can record all three signals in real-time. Initial results for signal interpretation come from a recurrent neural network (RNN) based machine learning algorithm, Long Short-Term Memory (LSTM), which is used to monitor and identify key features in the ECG data. The next phase of our research will include cross-examination of all three sensor signals, development of machine learning algorithms for PCG and SCG signals, and continuous improvement of the wearable device

Implementação de algoritmos de TensorFlow™ para detetar patologias cardíacas

Dissertação de mestrado em Engenharia Eletrónica Industrial e ComputadoresAs doenças cardiovasculares são umas das principais doenças que prejudicam a saúde humana, atraindo cada vez mais atenção da comunidade medica. É portanto necessário que os pacientes possam fazer um exame cardiovascular em qualquer instante, sítio e com um resultado preciso no momento. É notar que muitos pacientes com estas doenças são vítimas de ataque cardíaco quando dormem e acabam por morrer por não serem resgatadas a tempo. Baseando este assunto como ponto de partida, esta dissertação foca-se no estudo do sinal do dispositivo SCG (“Seismocardiogram”), o dispositivo registra informações em tempo real do coração através de sensores sem fazer contacto com o corpo, depois utiliza tecnologia de inteligência artificial para analisar as informações registadas e alerta sobre a possível ocorrência de problemas cardíacos. O sinal da SCG é imperfeição, apesar de ser inicialmente processado por um circuito de hardware, ainda pode conter ruído, com valores incorretos e inconsistentes. Portanto, antes de trabalhar com o sinal, deve-se utilizar a técnica de pré-processamento de sinais para melhorar a qualidade dos sinais por via da eliminação e filtragem. “Wavelet transform” ou Passa banda é um método que pode efetivamente eliminar o ruído. Outro problema da atualidade, é conseguir identificar os tipos dos problemas cardíacos de forma atempada e eficiente. Devido à necessidade de analisar uma grande quantidade de dados, torna-se difícil conseguir obter estas métricas (identificação e eficiência) e consequentemente ajudar resolver este problema que abrange em grande escala a população mundial. Contudo nos últimos anos, o surgimento de métodos como “Machine Learning” permite fazer uma melhor prevenção sobre a possível ocorrência de problemas cardíacos, tendo-se tornado um das melhores métodos desenvolvidos para tal. Nesta dissertação pretende-se desenvolver um sistema que recolha dados de uma SCG “Seismocardiogram”, que integre um acelerómetro MEMS baseado na medição de tempos de “pull-in”, tais como batimentos cardíacos e respiração. Também se pretende desenvolver uma aplicação que utilize “Machine Learning” para reconhecer sinal de SCG. Outro objetivo é utilizar a biblioteca “Open Source”de “TensorFlow” para implementar um algoritmo, que seja capaz de analisar e prever a evolução de cada doente. Desta forma, é possível ter um sistema que, através do histórico de saúde e dos dados recolhidos do doente, possibilite aos médicos detetarem mais facilmente um problema cardíaco que o paciente tenha.Cardiovascular diseases are one of the major threats to human health, attracting more and more attention from the medical community. It is therefore necessary that patients are able to take a cardiovascular examination at any time, place and with a precise result immediatley. It is noteworthy that many patients with these diseases are victims of heart attacks when they sleep and end up dying for not being rescued in time. Based on this subject as a starting point, this dissertation focuses on the study of the signal of a SCG device (Seismocardiogram), a device that registers real-time information from the heart through sensors without making contact with the body, after use artificial intelligence technology to analyze the information recorded and warns about the possible occurrence of heart problems. The SCG is imperfections, and although it is initially processed by an hardware circuit, it may still contain noise, with incorrect and inconsistent values. Therefore, prior to working with the signal, a signal preprocessing technique should be used to improve signal quality through elimination and filtering. Wavelet transform or Passband are methods that can be effectively used to eliminate noise. Another problem today, is the abiliyt to identify the types of diseases in a timely and efficient manner. Due to the need of analyzing a large amount of data, it is difficult to obtain these metrics (forecasting and efficiency) and consequently to solve this problem that covers the world population on a large scale. However, in recent years, the emergence of methods such as Machine Learning allow for better prevention of the possible occurrence of cardiac problems and has become one of the best methods developed for this. This thesis intends to develop a system that collects data from an SCG Seismocardiogram which integrates a MEMS accelerometer based on the measurement of pull-in times such as heart rate and respiration. It also intends to develop an application using Machine Learning to recognize an SCG signal. Another goal is to use the TensorFlow Open Text library to implement an algorithm capable of analyzing and predicting the evolution of each patient. In this way, it is possible to have a system that, through the history of health and data collected from the patient, enables physicians to easily detect a cardiac problem that the patient might have

Seismocardiography - Genesis, and Utilization of Machine Learning for Variability Reduction and Improved Cardiac Health Monitoring

Seismocardiography (SCG) is the measured chest surface vibrations resulting from cardiac activity. Although SCG can contain information that correlate with cardiac health, its utility may be limited by lack of understanding of the signal genesis and a variability that can mask subtle SCG changes. The current research utilized medical imaging reconstruction and finite element method (FEM) to simulate SCG by modeling the propagation of myocardial movements to the chest surface. FEM analysis provided a link between myocardial movements and the SCG signals measured at the chest surface and suggested that myocardial movement is a primary source of SCG. Increased understanding of the sources and propagation of SCG may help increase the utility of SCG as a cardiac monitoring tool. To reduce the variability of SCG measured in human subjects, unsupervised machine learning (ML) was implemented to group SCG beats into clusters with minimal intra-cluster heterogeneity. The clustering helped reduce the SCG variability and unveiled consistent relations with the respiratory phases and SCG morphology. This clustering reduced noise in calculating signal features and provided additional useful features. The study also analyzed longitudinal SCG from heart failure (HF) patients in order to predict HF readmission. Here, many time- and frequency-domain SCG features were extracted. Certain features showed good correlations with readmission. Using supervised ML algorithms, high classification accuracies (up to 100%) were achieved suggesting high SCG utility for monitoring HF patients and possibly other heart conditions. Effective monitoring followed by timely intervention can lead to improved patient management and reduced mortality

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices