Efficient and secured wireless monitoring systems for detection of cardiovascular diseases

Cardiovascular Disease (CVD) is the number one killer for modern era. Majority of the deaths associated with CVD can entirely be prevented if the CVD struck person is treated with urgency. This thesis is our effort in minimizing the delay associated with existing tele-cardiology application. We harnessed the computational power of modern day mobile phones to detect abnormality in Electrocardiogram (ECG). If abnormality is detected, our innovative ECG compression algorithm running on the patient's mobile phone compresses and encrypts the ECG signal and then performs efficient transmission towards the doctors or hospital services. According to the literature, we have achieved the highest possible compression ratio of 20.06 (95% compression) on ECG signal, without any loss of information. Our 3 layer permutation cipher based ECG encoding mechanism can raise the security strength substantially higher than conventional AES or DES algorithms. If in near future, a grid of supercomputers can compare a trillion trillion trillion (1036) combinations of one ECG segment (comprising 500 ECG samples) per second for ECG morphology matching, it will take approximately 9.333 X 10970 years to enumerate all the combinations. After receiving the compressed ECG packets the doctor's mobile phone or the hospital server authenticates the patient using our proposed set of ECG biometric based authentication mechanisms. Once authenticated, the patients are diagnosed with our faster ECG diagnosis algorithms. In a nutshell, this thesis contains a set of algorithms that can save a CVD affected patient's life by harnessing the power of mobile computation and wireless communication

A Framework for Remote Patient Monitoring to Diagnose the Cardiac Disorders

Electrocardiogram (ECG) is an efficient diagnostic tool to monitor the electrical activity of heart. One of the most vital benefit of using telecommunication technologies in medical field is to provide cardiac health care at a distance. Telecardiology is the most efficient way to provide faster and affordable health care for the cardiac patients located at rural areas. Early detection of cardiac disorders can minimize cardiac death rates. In real time monitoring process, ECG data from a patient usually takes large storage space in the order of gigabytes (GB). Hence, compression of bulky ECG signal is a common requirement for faster transmission of cardiac signals using wireless technologies. Several techniques such as the Fourier transform based methods, wavelet transform based methods, etc., have been reported for compression of ECG data. Though Fourier transform is suitable for analyzing the stationary signals. An improved version, the wavelet transform allows the analysis of non-stationary signal. It provides a uniform resolution for all the scales, however, wavelet transform faces difficulties like uniformly poor resolution due to limited size of the basic wavelet function and it is nonadaptive in nature. A data adaptive method to analyse non-stationary signal is based on empirical mode decomposition (EMD), where the bases are derived from the multivariate data which are nonlinear and non-stationary. A new ECG signal compression technique based on EMD is proposed, in which first EMD technique is applied to decompose the ECG signal into several intrinsic mode functions (IMFs). Next, downsampling, discrete cosine transform (DCT), window filtering and Huffman encoding processes are used sequentially to compress the ECG signal. The compressed ECG is then transmitted as short messageservice (SMS) message using a global system for mobile communications (GSM) modem. First the AT-command ‘+CMGF’ is used to set the SMS to text mode. Next, the GSM modem uses the AT-command ‘+CMGS’ to send a SMS message. The received text SMS messages are transferred to a personal computer (PC) using blue-tooth. All text SMS messages are combined in PC as per the received sequence and fed as data input to decompress the compressed ECG data. The decompression method which is used to reconstruct the original ECG signal consists of Huffman decoding, inverse discrete cosine transform (IDCT) and spline interpolation. The performance of the compression and decompression techniques are evaluated in terms of compression ratio (CR) and percent root mean square difference (PRD) respectively by using both European ST-T database and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. The average values of CR and PRD for selected ECG records of European ST-T database are found to be 23.5:1 and 1.38 respectively. All 48 ECG records of MIT-BIH arrhythmia database are used for comparison purpose and the average values of CR and PRD are found to be 23.74:1 and 1.49 respectively. The reconstructed ECG signal is then used for detection of cardiac disorders like bradycardia, tachycardia and ischemia. The preprocessing stage of the detection technique filters the normalized signal to reduce noise components and detects the QRS-complexes. Next, ECG feature extraction, ischemic beat classification and ischemic episode detection processes are applied sequentially to the filtered ECG by using rule based medical knowledge. The ST-segment and T-wave are the two features generally used for ischemic beat classification. As per the recommendation of ESC (European Society of cardiology) the ischemic episode detection procedure considers minimum 30s duration of signal. The performance of the ischemic episode detection technique is evaluated in terms of sensitivity (Se) and positive predictive accuracy (PPA) by using European ST-T database. This technique achieves an average Se and PPA of 83.08% and 92.42% respectively

Cardiovascular data analytics for real time patient monitoring

Improvements in wearable sensor devices make it possible to constantly monitor physiological parameters such as electrocardiograph (ECG) signals for long periods. Remote patient monitoring with wearable sensors has an important role to play in health care, particularly given the prevalence of chronic conditions such as cardiovascular disease (CVD)—one of the prominent causes of morbidity and mortality worldwide. Approximately 4.2 million Australians suffer from long-term CVD with approximately one death every 12 minutes. The assessment of ECG features, especially heart rate variability (HRV), represents a non-invasive technique which provides an indication of the autonomic nervous system (ANS) function. Conditions such as sudden cardiac death, hypertension, heart failure, myocardial infarction, ischaemia, and coronary heart disease can be detected from HRV analysis. In addition, the analysis of ECG features can also be used to diagnose many types of life-threatening arrhythmias, including ventricular fibrillation and ventricular tachycardia. Non-cardiac conditions, such as diabetes, obesity, metabolic syndrome, insulin resistance, irritable bowel syndrome, dyspepsia, anorexia nervosa, anxiety, and major depressive disorder have also been shown to be associated with HRV. The analysis of ECG features from real time ECG signals generated from wearable sensors provides distinctive challenges. The sensors that receive and process the signals have limited power, storage and processing capacity. Consequently, algorithms that process ECG signals need to be lightweight, use minimal storage resources and accurately detect abnormalities so that alarms can be raised. The existing literature details only a few algorithms which operate within the constraints of wearable sensor networks. This research presents four novel techniques that enable ECG signals to be processed within the limitations of resource constraints on devices to detect some key abnormalities in heart function. - The first technique is a novel real-time ECG data reduction algorithm, which detects and transmits only those key points that are critical for the generation of ECG features for diagnoses. - The second technique accurately predicts the five-minute HRV measure using only three minutes of data with an algorithm that executes in real-time using minimal computational resources. - The third technique introduces a real-time ECG feature recognition system that can be applied to diagnose life threatening conditions such as premature ventricular contractions (PVCs). - The fourth technique advances a classification algorithm to enhance the performance of automated ECG classification to determine arrhythmic heart beats based on noisy ECG signals. The four novel techniques are evaluated in comparison with benchmark algorithms for each task on the standard MIT-BIH Arrhythmia Database and with data generated from patients in a major hospital using Shimmer3 wearable ECG sensors. The four techniques are integrated to demonstrate that remote patient monitoring of ECG using HRV and ECG features is feasible in real time using minimal computational resources. The evaluation show that the ECG reduction algorithm is significantly better than existing algorithms that can be applied within sensor nodes, such as time-domain methods, transformation methods and compressed sensing methods. Furthermore, the proposed ECG reduction is found to be computationally less complex for resource constrained sensors and achieves higher compression ratios than existing algorithms. The prediction of a common HRV measure, the five-minute standard deviation of inter-beat variations (SDNN) and the accurate detection of PVC beats was achieved using a Count Data Model, combined with a Poisson-generated function from three-minute ECG recordings. This was achieved with minimal computational resources and was well suited to remote patient monitoring with wearable sensors. The PVC beats detection was implemented using the same count data model together with knowledge-based rules derived from clinical knowledge. A real-time cardiac patient monitoring system was implemented using an ECG sensor and smartphone to detect PVC beats within a few seconds using artificial neural networks (ANN), and it was proven to provide highly accurate results. The automated detection and classification were implemented using a new wrapper-based hybrid approach that utilized t-distributed stochastic neighbour embedding (t-SNE) in combination with self-organizing maps (SOM) to improve classification performance. The t-SNE-SOM hybrid resulted in improved sensitivity, specificity and accuracy compared to most common hybrid methods in the presence of noise. It also provided a better, more accurate identification for the presence of many types of arrhythmias from the ECG recordings, leading to a more timely diagnosis and treatment outcome.Doctor of Philosoph

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

Telemedicine Pre and Post Covid-19: Lessons for Commercialisation Based on Previous Use Cases

Telemedicine used to be slow, difficult, expensive and widely neglected by doctors and patients. COVID-19 changed everything; telemedicine is entering a period of rapid economic and business growth. This paper discusses the reasons for change in telemedicine over the last 20 years, through real-life medical technology projects, telemetry, ehealth and health IT. Our methods are based on the analysis of telemedicine projects we have implemented and characteristic historical data.  The results of our investigation demonstrate a clear increase of significance in telemedicine in the present and near future. We envision the evolution of mobile phones to personal telehealth monitors.  Prior to COVID-19, market penetration and economic factors of telemedicine evolved slowly and in an uneven manner on a global scale. Many of the projects remained active only as long as the grant or corporate or national support was provided. The age of novel globally spreading infectious diseases, exemplified by COVID-19, has created an unusual, different setting. Recent pandemics and epidemics have changed global economics significantly and generated a new motivation and a new market with a projected trillion-dollar market value. Post COVID-19, regular and periodic epidemics and pandemics are expected to continue to occur. This will generate an enormous global market for isolated high-tech services, including telemedicine and telemetry

IoMT-based biomedical measurement systems for healthcare monitoring: a review

Biomedical measurement systems (BMS) have provided new solutions for healthcare monitoring and the diagnosis of various chronic diseases. With a growing demand for BMS in the field of medical applications, researchers are focusing on advancing these systems, including Internet of Medical Things (IoMT)-based BMS, with the aim of improving bioprocesses, healthcare systems and technologies for biomedical equipment. This paper presents an overview of recent activities towards the development of IoMT-based BMS for various healthcare applications. Different methods and approaches used in the development of these systems are presented and discussed, taking into account some metrological aspects related to the requirement for accuracy, reliability and calibration. The presented IoMT-based BMS are applied to healthcare applications concerning, in particular, heart, brain and blood sugar diseases as well as internal body sound and blood pressure measurements. Finally, the paper provides a discussion about the shortcomings and challenges that need to be addressed along with some possible directions for future research activities.</p

Wrist-based Phonocardiogram Diagnosis Leveraging Machine Learning

With the tremendous growth of technology and the fast pace of life, the need for instant information has become an everyday necessity, more so in emergency cases when every minute counts towards saving lives. mHealth has been the adopted approach for quick diagnosis using mobile devices. However, it has been challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this research is to diagnose the heart condition based on phonocardiogram (PCG) analysis using Machine Learning techniques assuming limited processing power, in order to be encapsulated later in a mobile device. The diagnosis of PCG is performed using two techniques; 1. parametric estimation with multivariate classification, particularly discriminant function. Which will be explored at length using different number of descriptive features. The feature extraction will be performed using Wavelet Transform (Filter Bank). 2. Artificial Neural Networks, and specifically Pattern Recognition. This will also use decomposed version of PCG using Wavelet Transform (Filter Bank). The results showed 97.33% successful diagnosis using the first technique using PCG with a 19 dB Signal-to-Noise-Ratio. When the signal was decomposed into four sub-bands using a Filter Bank of the second order. Each sub-band was described using two features; the signal’s mean and covariance. Additionally, different Filter Bank orders and number of features are explored and compared. Using the second technique the diagnosis resulted in a 100% successful classification with 83.3% trust level. The results are assessed, and new improvements are recommended and discussed as part of future work.Teknologian valtavan kehittymisen ja nopean elämänrytmin myötä välittömästi saatu tieto on noussut jokapäiväiseksi välttämättömyydeksi, erityisesti hätätapauksissa, joissa jokainen säästetty minuutti on tärkeää ihmishenkien pelastamiseksi. Mobiiliterveys, eli mHealth, on yleisesti valjastettu käyttöön nopeaksi diagnoosimenetelmäksi mobiililaitteiden avulla. Käyttö on kuitenkin ollut haastavaa korkean datan laatuvaatimuksen ja suurten tiedonkäsittelyvaatimuksien, nopean laskentatehon ja sekä suuren virrankulutuksen vuoksi. Tämän tutkimuksen tavoitteena oli diagnosoida sydänsairauksia fonokardiogrammianalyysin (PCG) perusteella käyttämällä koneoppimistekniikoita niin, että käytettävä laskentateho rajoitetaan vastaamaan mobiililaitteiden kapasiteettia. PCG-diagnoosi tehtiin käyttäen kahta tekniikkaa 1. Parametrinen estimointi käyttäen moniulotteista luokitusta, erityisesti signaalien erotteluanalyysin avulla. Tätä asiaa tutkittiin syvällisesti käyttäen erilaisia tilastotieteellisesti kuvailevia piirteitä. Piirteiden irrotus suoritettiin käyttäen Wavelet-muunnosta ja suodatinpankkia. 2. Keinotekoisia neuroverkkoja ja erityisesti hahmontunnistusta. Tässä menetelmässä käytetään myös PCG-signaalin hajoitusta ja Wavelet-muunnos -suodatinpankkia. Tulokset osoittivat, että PCG 19dB:n signaali-kohina-suhteella voi johtaa 97,33% onnistuneeseen diagnoosiin käytettäessä ensimmäistä tekniikkaa. Signaalin hajottaminen neljään alikaistaan suoritettiin käyttämällä toisen asteen suodatinpankkia. Jokainen alikaista kuvattiin käyttäen kahta piirrettä: signaalin keskiarvoa ja kovarianssia, näin saatiin yhteensä kahdeksan ominaisuutta kuvaamaan noin yhden minuutin näytettä PCG-signaalista. Lisäksi tutkittiin ja verrattiin eriasteisia suodattimia ja piirteitä. Toista tekniikkaa käyttäen diagnoosi johti 100% onnistuneeseen luokitteluun 83,3% luotettavuustasolla. Tuloksia käsitellään ja pohditaan, sekä tehdään niistä johtopäätöksiä. Lopuksi ehdotetaan ja suositellaan käytettyihin menetelmiin uusia parannuksia jatkotutkimuskohteiksi.fi=vertaisarvioitu|en=peerReviewed

Data semantic enrichment for complex event processing over IoT Data Streams

This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation