Machine learning for the classification of atrial fibrillation utilizing seismo- and gyrocardiogram

A significant number of deaths worldwide are attributed to cardiovascular diseases (CVDs), accounting for approximately one-third of the total mortality in 2019, with an estimated 18 million deaths. The prevalence of CVDs has risen due to the increasing elderly population and improved life expectancy. Consequently, there is an escalating demand for higher-quality healthcare services. Technological advancements, particularly the use of wearable devices for remote patient monitoring, have significantly improved the diagnosis, treatment, and monitoring of CVDs. Atrial fibrillation (AFib), an arrhythmia associated with severe complications and potential fatality, necessitates prolonged monitoring of heart activity for accurate diagnosis and severity assessment. Remote heart monitoring, facilitated by ECG Holter monitors, has become a popular approach in many cardiology clinics. However, in the absence of an ECG Holter monitor, other remote and widely available technologies can prove valuable. The seismo- and gyrocardiogram signals (SCG and GCG) provide information about the mechanical function of the heart, enabling AFib monitoring within or outside clinical settings. SCG and GCG signals can be conveniently recorded using smartphones, which are affordable and ubiquitous in most countries. This doctoral thesis investigates the utilization of signal processing, feature engineering, and supervised machine learning techniques to classify AFib using short SCG and GCG measurements captured by smartphones. Multiple machine learning pipelines are examined, each designed to address specific objectives. The first objective (O1) involves evaluating the performance of supervised machine learning classifiers in detecting AFib using measurements conducted by physicians in a clinical setting. The second objective (O2) is similar to O1, but this time utilizing measurements taken by patients themselves. The third objective (03) explores the performance of machine learning classifiers in detecting acute decompensated heart failure (ADHF) using the same measurements as O1, which were primarily collected for AFib detection. Lastly, the fourth objective (O4) delves into the application of deep neural networks for automated feature learning and classification of AFib. These investigations have shown that AFib detection is achievable by capturing a joint SCG and GCG recording and applying machine learning methods, yielding satisfactory performance outcomes. The primary focus of the examined approaches encompassed (1) feature engineering coupled with supervised classification, and (2) iv automated end-to-end feature learning and classification using deep convolutionalrecurrent neural networks. The key finding from these studies is that SCG and GCG signals reliably capture the heart’s beating pattern, irrespective of the operator. This allows for the detection of irregular rhythm patterns, making this technology suitable for monitoring AFib episodes outside of hospital settings as a remote monitoring solution for individuals suspected to have AFib. This thesis demonstrates the potential of smartphone-based AFib detection using built-in inertial sensors. Notably, a short recording duration of 10 to 60 seconds yields clinically relevant results. However, it is important to recognize that the results for ADHF did not match the state-of-the-art achievements due to the limited availability of ADHF data combined with arrhythmias as well as the lack of a cardiopulmonary exercise test in the measurement setting. Finally, it is important to recognize that SCG and GCG are not intended to replace clinical ECG measurements or long-term ambulatory Holter ECG recordings. Instead, within the scope of our current understanding, they should be regarded as complementary and supplementary technologies for cardiovascular monitoring

Outlier Detection in Weight Time Series of Connected Scales: A Comparative Study

Smart and connected health technologies as part of the digitally supporting health and heathcare plans can play an explicitly important role in improving preventive healthcare and patient outcomes, decreasing costs, and speeding up the scientific discoveries. Rigorous information processing approaches, such as outlier detection and data cleaning, are therefore needed to enhance the reliability of the acquired data. A "smart electronic weight scale" is a connected sensor that regularly measures and stores time series of body mass values. The long-term self-weighing time series data, like any other time series data, may occasionally contain abnormal values which are called "outliers". The existence of these outlying values can distort or mislead the data analysis. In this thesis, detection of outliers in time series of weight measurements of 10,000 anonymous Withings weight scale users is investigated. Four point-wise outlier detection approaches are studied and compared from different aspects. These techniques are: (1) a method based on Autoregressive Integrated Moving Average (ARIMA) time series modelling, (2) moving Median Absolute Deviation (MAD) scale estimate, (3) conventional Rosner statistic, and (4) windowed Rosner statistic. The results suggest that ARIMA approach, moving MAD and windowed Rosner statistic can properly find the outliers; however, in case of facing missing data the only method which was able to ideally identify the outliers was ARIMA approach. In contrast, conventional Rosner statistic did not show acceptable outlier detection power. The computational complexity of the ARIMA approach was unsatisfactorily costly, whilst the rest of the tested techniques were quite fast in terms of computation time

Investigating Nano-Coated Surfaces in Improvement Wear Resistance of Tillage Tools

The blade in the tillage operation has the most interaction with soil particles. It causes the wear and tear of this piece and reduces its expected life span. Tillage tool wear is of great importance to farmers in terms of economic aspects, reduced quality tillage operations, and increased power consumption. The aim of this study is to investigate wearing of five materials including st37 steel (SST37) plate, galvanized steel (GAS), and fiberglass (GFRP), and two coatings (i.e., titanium nano-nitride (nano-TiN) and tantalum carbide (nano-TaC)) by sputtering in the plasma medium of the layer based on conventional steel. These pieces were tested in three types of light, medium, and heavy soil textures. To assess tool wear, a rotating soilbin was developed. The level of operation of the blades was evaluated in three steps of 500 m and a total of 1500 m with a benchmark of weight loss due to deterioration in the assessment of laboratory operations. There was a significant difference between different treatments. The important point of this research is that the nano-coated blades, owing to their superior properties and the smooth surfaces, showed the least wear. Their abrasion resistance is about 7 times that of the uncoated steel, 5.5 times that of galvanized steel. Fiberglass with reinforced polymer fibers showed a good performance against ordinary and galvanized steel against abrasion

An activity recognition framework deploying the random forest classifier and a single optical heart rate monitoring and triaxial accelerometer wrist-band

Wrist-worn sensors have better compliance for activity monitoring compared to hip, waist, ankle or chest positions. However, wrist-worn activity monitoring is challenging due to the wide degree of freedom for the hand movements, as well as similarity of hand movements in different activities such as varying intensities of cycling. To strengthen the ability of wrist-worn sensors in detecting human activities more accurately, motion signals can be complemented by physiological signals such as optical heart rate (HR) based on photoplethysmography. In this paper, an activity monitoring framework using an optical HR sensor and a triaxial wrist-worn accelerometer is presented. We investigated a range of daily life activities including sitting, standing, household activities and stationary cycling with two intensities. A random forest (RF) classifier was exploited to detect these activities based on the wrist motions and optical HR. The highest overall accuracy of 89.6 ± 3.9% was achieved with a forest of a size of 64 trees and 13-s signal segments with 90% overlap. Removing the HR-derived features decreased the classification accuracy of high-intensity cycling by almost 7%, but did not affect the classification accuracies of other activities. A feature reduction utilizing the feature importance scores of RF was also carried out and resulted in a shrunken feature set of only 21 features. The overall accuracy of the classification utilizing the shrunken feature set was 89.4 ± 4.2%, which is almost equivalent to the above-mentioned peak overall accuracy.</p

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI) : Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.Peer reviewe

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI): Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments.Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns.Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings.Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD.Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders

Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms

Atrial fibrillation (AFib) is the most common sustained heart arrhythmia and is characterized by irregular and excessively frequent ventricular contractions. Early diagnosis of AFib is a key step in the prevention of stroke and heart failure. In this study, we present a comprehensive time-frequency pattern analysis approach for automated detection of AFib from smartphone-derived seismocardiography (SCG) and gyrocardiography (GCG) signals. We sought to assess the diagnostic performance of a smartphone mechanocardiogram (MCG) by considering joint SCG-GCG recordings from 435 subjects including 190 AFib and 245 sinus rhythm (SR) cases. A fully automated AFib detection algorithm consisting of various signal processing and multidisciplinary feature engineering techniques was developed and evaluated through a large set of cross-validation (CV) data including 300 (AFib=150) cardiac patients. The trained model was further tested on an unseen set of recordings including 135 (AFib=40) subjects considered as cross-database (CD). The experimental results showed accuracy, sensitivity, and specificity of approximately 97%, 99%, and 95% for the CV study and up to 95%, 93%, and 97% for the CD test, respectively. The F1 scores were 97% and 96% for the CV and CD, respectively. A positive predictive value of approximately 95% and 92% was obtained respectively for the validation and test sets suggesting high reproducibility and repeatability for mobile AFib detection. Moreover, the kappa coefficient of the method was 0.94 indicating a near-perfect agreement in rhythm classification between the smartphone algorithm and visual interpretation of telemetry recordings. The results support the feasibility of self-monitoring via easy-to-use and accessible MCGs.</p

Outlier Detection in Weight Time Series of Connected Scales: A Comparative Study

Smart and connected health technologies as part of the digitally supporting health and heathcare plans can play an explicitly important role in improving preventive healthcare and patient outcomes, decreasing costs, and speeding up the scientific discoveries. Rigorous information processing approaches, such as outlier detection and data cleaning, are therefore needed to enhance the reliability of the acquired data. A "smart electronic weight scale" is a connected sensor that regularly measures and stores time series of body mass values. The long-term self-weighing time series data, like any other time series data, may occasionally contain abnormal values which are called "outliers". The existence of these outlying values can distort or mislead the data analysis. In this thesis, detection of outliers in time series of weight measurements of 10,000 anonymous Withings weight scale users is investigated. Four point-wise outlier detection approaches are studied and compared from different aspects. These techniques are: (1) a method based on Autoregressive Integrated Moving Average (ARIMA) time series modelling, (2) moving Median Absolute Deviation (MAD) scale estimate, (3) conventional Rosner statistic, and (4) windowed Rosner statistic. The results suggest that ARIMA approach, moving MAD and windowed Rosner statistic can properly find the outliers; however, in case of facing missing data the only method which was able to ideally identify the outliers was ARIMA approach. In contrast, conventional Rosner statistic did not show acceptable outlier detection power. The computational complexity of the ARIMA approach was unsatisfactorily costly, whilst the rest of the tested techniques were quite fast in terms of computation time