An ECG compression scheme based on vector quantisation

An ECG signal compression scheme based on the vector quantisation (VQ) method is proposed. The compression is performed by quantising the ECG samples into a reduced set of reference vectors, the codebook. The vector representing elements with a high frequency of occurrence is selected for further coding, while the rest of the signal containing all the values occurring with lower probabilities are kept unchanged. The proposed scheme is superior to other available direct compression methods in retaining the most important part of the ECG signal

Detection and extraction of the ECG signal parameters

This work investigates a set of efficient techniques to extract important features from the ECG data applicable in automatic cardiac arrhythmia classification. The selected parameters are divided into two main categories namely morphological and statistical features. Extraction of morphological features was achieved using signal processing techniques and detection of statistical features was performed by employing mathematical methods. Each specific method was applied to a pre-selected data segment of the MIT-BIH database. The classification of different heart beats was performed based upon the extracted features. The morphological features were found as the most efficient for further ECG signal analysis. However, because of ECG signal variability in different patients, the mathematical approach is preferred for a precise and robust feature extraction. As a result of the extracted features, an efficient computer based ECG signal classifier could be developed for detection of a vast range of cardiac arrhythmias

ECG noise cancellation using digital filters

A digital filter structure is proposed to maximally remove noise from the ECG signals. This structure is based on cascading a zero-phase bandpass, an adaptive filter, and multi-band-pass filter. It provides an efficient method for removing noise from the ECG signals. This filter structure has low implementation complexity and introduces little noise into a typical ECG. It can be applied to real-time applications particularly automatic cardiac arrhythmia classifiers

A Cox-based Model for Predicting the Risk of Cardiovascular Disease

This research is aimed to develop a 10-year risk prediction model and identify key contributing Cardiovascular Disease (CVD) risk factors. A Cox proportional hazard regression method was adopted to design and develop the risk model. We used Framingham Original Cohort dataset of 5079 men and women aged 30 - 62 years, who had no overt symptoms of CVD at the baseline. Out of them, 3189 (62.78%) had an actual CVD event. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure, cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel contributing risk factors. We validated the model via statistical and empirical validation methods. The proposed model achieved an acceptable discrimination and calibration with C-index (receiver operating characteristic (ROC)) being 0.71 from the validation dataset

Current Challenges and Barriers to the Wider Adoption of Wearable Sensor Applications and Internet-of-Things in Health and Well-being

The aim of this review is to investigate barriers and challenges of Wearable Sensors (WS) and Internet-of-Things (IoT) solutions in healthcare. This work specifically focuses on falls and Activity of Daily Life (ADLs) for ageing population and independent living for older adults. The majority of the studies focussed on the system aspects of WS and IoT solutions including advanced sensors, wireless data collection, communication platforms and usability. The current studies are focused on a single use-case/health area using non-scalable and ‘silo’ solutions. Moderate to low usability/ userfriendly approach is reported in most of the current studies. Other issues found were, inaccurate sensors, battery/power issues, restricting the users within the monitoring area/space and lack of interoperability. The advancement of wearable technology and possibilities of using advanced technology to support ageing population is a concept that has been investigated by many studies. We believe, WS and IoT monitoring plays a critical role towards support of a world-wide goal of tackling ageing population and efficient independent living. Consequently, in this study we focus on identifying three main challenges regarding data collection and processing, techniques for risk assessment, usability and acceptability of WS and IoT in wider healthcare settings

Blood Pressure Estimation from Electrocardiogram and Photoplethysmography Signals Using Continuous Wavelet Transform and Convolutional Neural Network

Cuff-less and continuous blood pressure (BP) measurement has recently become an active research area in the field of remote healthcare monitoring. There is a growing demand for automated BP estimation and monitoring for various long-term and chronic conditions. Automated BP monitoring can produce a good amount of rich health data, which increases the chance of early diagnosis and treatments that are critical for a long-term condition such as hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data is challenging, which is aimed to address in this research. We employed a continuous wavelet transform (CWT) and a deep convolutional neural network (CNN) to estimate the BP. The electrocardiogram (ECG), photoplethysmography (PPG) and arterial blood pressure (ABP) signals were extracted from the online Medical Information Mart for Intensive Care (MIMIC III) database. The scalogram of each signal was created and used for training and testing our proposed CNN model that can implicitly learn to extract the descriptive features from the training data. This study achieved a promising BP estimation approach has been achieved without employing engineered feature extraction that is comparable with previous works. Experimental results demonstrated a low root mean squere error (RMSE) rate of 3.36 mmHg and a high accuracy of 86.3% for BP estimations. The proposed CNN-based model can be considered as a reliable and feasible approach to estimate BP for continuous remote healthcare monitoring

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals

Continuous blood pressure (BP) measurement is vital in monitoring patients’ health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 ± 2.45 mmHg (MAE ± STD) for SBP and 3.08 ± 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard