Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

Assessment of the Effectiveness of Kinetic Chain Approach for Primary Adhesive Capsulitis of Shoulder- An Experimental Study

Introduction: Idiopathic adhesive capsulitis is characterised by gradual onset of pain in shoulder and its limited mobility of motion due to a thickened capsule with an unknown cause. The peak incidence rate is between the ages of 40 and 60 years with less chances of occurrence in the younger group and individuals, who engage in physical labour are far less likely to experience it. In order to determine whether the kinetic chain approach is more effective, researchers tested it on individuals with adhesive capsulitis. Aim: To assess effectiveness of the kinetic chain approach for treating primary adhesive capsulitis. Materials and Methods: This pre and postexperimental study was conducted at the Department of Physiotherapy, Santosh Medical College and Hospital, Ghaziabad, Uttar Pradesh, India, for a period of one year from January 2020 to December 2020. A total of 60 patients with primary adhesive capsulitis were included in the trial and were divided into Group-A and Group-B using a systematic random sampling process. Group A received traditional physiotherapy in addition to the Kinetic chain technique. In Group B, regular physiotherapy was the only treatment provided to patients. The Range of Motion (ROM), Visual Analog Scale (VAS) and the Shoulder Pain and Disability Index (SPADI) were measured in both groups pre, mid and post-treatment groups. Non parametric statistical techniques were used to analyse the data owing its originality. Results: A total of 30 patients in each Group (A and B) were provided with Kinetic chain approach (mean age=53.93 years) and conventional physiotherapy (mean age=55.40 years), respectively. For comparing data within the same group, Wilcoxon signed-ranks test was used for this. Mann Whitney U-test was used to compare the data between groups. The p-values for the VAS, SPADI, and ROM showed statistical significance at the 0.001 level. In terms of pain, functional impairment, and range of motion, with Group A showing greater strides than Group B. Conclusion: The combination of kinetic chain technique and traditional physiotherapy was more beneficial than either group when used singularly to reduce discomfort, increase the range of motion, and enhance functional capacity in adhesive capsulitis patients

A novel positioning method for magnetic spiral-type capsule endoscope using an adaptive LMS algorithm

In this paper, a novel positioning method for the wireless capsule endoscope (WCE) is proposed. An up-down symmetric array of magnetic sensors is used to detect magnetic signals from an external permanent magnet (EPM) for active control and mixed magnetic signals (the EPM and the WCE), and the adaptive least mean squares (LMS) algorithm is applied. Firstly, the number of iterations is determined by comparing the cancellation effect of input signals of different lengths. Subsequently, to separate the WCE's magnetic signals from the mixed magnetic signals, the data obtained from the magnetic sensor arrays are processed in weighted iterations. The method has been applied to the actual experimental platform. From the experimental results achieved in this work, the average relative errors of the WCE's triaxial signals were found to be 2.04 %, 2.20 %, and 1.47 % respectively. Achieved results demonstrate the feasibility and rationality of the positioning method discussed in this work. Moreover, the method can solve the problem of strong magnetic interference when the EPM provides active control to the WCE. It plays an essential role in driving the realization of closed-loop control of the WCE

Closed-loop active control of the magnetic capsule endoscope with a robotic arm based on image navigation

This paper proposes a novel approach for the closed-loop active control of the magnetic capsule endoscope based on image navigation. The proposed method uses a robotic arm to control the rotation of an external permanent magnet (EPM) to generate a rotating magnetic field. Subsequently, this rotating magnetic field controls the motion of the magnetic capsule endoscope (MCE). A gyroscope was included inside the MCE to obtain its posture. Using the image captured by the camera installed in the MCE, a relationship between the relative position of the MCE and the intestine was established. Based on the relative position relationship and the MCE's posture, the MCE was controlled to move along the intestinal center by the robotic arm, thus, the closed-loop active control of the MCE was achieved. Furthermore, the feasibility of closed-loop active control of the MCE was verified through the isolated porcine small intestine experiment. Experimental results show that the closed-loop active control combined with image navigation is not only easy to operate, but also offers high stability in terms of control mechanism, and suitability for use in clinical applications

Innovative Design of Dam Water Level Sensor

An innovative design of a water level sensor is reported in this paper. The proposed design is suitable for high precision measurements usually required in sensing the water level of dams, tanks & reservoirs. This paper employs a reflected float mechanism in the presented design which helps in sensing the level to be measured. The proposed sensor design has a simple, reliable and low cost design concept and ease of installation. For precise measurement, the presented design has been calibrated and tested for level measurement up to 225 cm and corresponding error have been considered. The error is under acceptable limits i.e. within ± 2 % of the measured value. The improvement in the precision value has been also reported in the paper. The design is suitable for measuring level in the range of 0.1 cm and it can be improved further as per the requirement, by simply varying the circuit parameters. Steps utilized to develop the presented design have been also mentioned to clearly present the design concept and required setup

Trigger Pulse Generator Using Proposed Buffered Delay Model and Its Application

This paper proposes a circuit capable of incorporating buffered delays in the order of picoseconds. To study our proposed circuit in the profound way, we have also explored our proposed circuit using emerging technologies such as FinFET and CNFET. Comparisons between these technologies have been made in terms of different parameters such as duration of incorporated delays (pulse width) and its variability with supply voltages. Further, this paper also proposes a trigger pulse generator by utilizing proposed buffered delay circuit as its basic element. Parametric results obtained for the proposed trigger pulse generator match different application specific requirements. These applications are also mentioned in this paper. The proposed trigger pulse generator requires very low supply voltage (700 mV) and also proves its effectiveness in terms of tunability of pulse width of the generated pulses. The modeling of the circuit has been done using Verilog and the simulation results are extensively verified using SPICE

Work-in-progress : remote learning and online experimentation for large cohorts

Teaching of Control and Automation usually faces multiple constraints such as the cost of hardware, limited access to the experimental setup, limitation in the parametrization of hardware etc. The emergence of digital technologies has allowed the development of cyber-physical systems. This paper explores the possibility of using a combination of physical hardware and virtual models to allow hybrid learning and practical experience with large cohorts of learners. The authors are currently exploring a multifaceted approach which is described as work in progress in this paper

Optimum supply voltage for high gain amplifier in telemetry circuitry for ultra-low power implantable cardiac pacemaker

This work investigates the deep subthreshold device size for the CMOS logic gate inverter circuit at the 16-nm technology node using a predictive technology model (PTM). The device's channel length (L) is established in order to acquire the optimal threshold voltage at 150 mV of the supply voltage. Using the same source voltage, the aspect ratio of an inverter logic circuit is determined, and the analysis of symmetrical transient responses is carried out. When the aspect ratio equals 3.52, the inverter logic gate is found to be symmetrical. The propagation delay is also extracted, and the achieved results demonstrate that a higher aspect ratio can facilitate high-performance circuit design. For the 16-nm technology node, the optimal threshold voltage is achieved at L=66 nm. In this work, the minimum energy point (MEP)/ Optimum Supply Voltage (OSV) is also attained at a supply voltage of 150 mV. MEP/OSV ensures improved performance and minimal power dissipation in the circuit. Based on the results achieved in this research work, a high-gain amplifier circuit in telemetry circuitry at MEP is designed and discussed. The functionality of the high-gain amplifier is verified at the optimum supply voltage