Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring

Abnormal vital signs often predict a serious condition of acutely ill hospital patients in 24 hours. The notable fluctuations of respiratory rate (RR) are highly predictive of deteriorations among the vital signs measured. Traditional methods of detecting RR are performed by directly measuring the air flow in or out of the lungs or indirectly measuring the changes of the chest volume. These methods require the use of cumbersome devices, which may interfere with natural breathing, are uncomfortable, have frequently moving artifacts, and are extremely expensive. This study aims to estimate the RR from electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which consist of passive and non-invasive acquisition modules. Algorithms have been validated by using PhysioNet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II)’s patient datasets. RR estimation provides the value of mean absolute error (MAE) for ECG as 1.25 bpm (MIMIC-II) and 1.05 bpm for the acquired data. MAE for PPG is 1.15 bpm (MIMIC-II) and 0.90 bpm for the acquired data. By using 1-minute windows, this method reveals that the filtering method efficiently extracted respiratory information from the ECG and PPG signals. Smaller MAE for PPG signals results from fewer artifacts due to easy sensor attachment for the PPG because PPG recording requires only one-finger pulse oximeter sensor placement. However, ECG recording requires at least three electrode placements at three positions on the subject’s body surface for a single lead (lead II), thereby increasing the artifacts. A reliable technique has been proposed for RR estimation

A NOVEL WAVEFORM MIRRORING TECHNIQUE FOR SYSTOLIC BLOOD PRESSURE ESTIMATION FROM ANACROTIC PHOTOPLETHYSMOGRAM

Continuous cuffless Blood Pressure (BP) measurement is an important tool to monitor the health of individuals at risk. In this study, a new method is proposed for Systolic BP (SBP) estimation utilizing Photoplethysmograms (PPG). To this end, toe and carotid PPG were recorded from seventeen subjects aged 20-28 years, whereas their SBP were measured using a standard BP cuff monitor for validation purpose. The proposed method is based on a novel mirroring technique, which allows for an accurate estimation of the Pulse Transit Time (PTT) from the PPG’s rising part (anacrotic) waveform using an ARX System Identification approach. Based on the modified Moens-Korteweg equation, SBP was then calculated based on the estimated PTT values obtained from the ARX model. The estimated PTT was found to be highly correlated to the measured SBP (R2 = 0.98). Comparison of calculated SBP to the measured SBP obtained using standard BP cuff monitor results in a mean error of 3.4%. Given that 95% of the estimated SBP values are accurate in the +/- 8 mmHg range, this method seems promising for non-invasive, continuous BP monitorin

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III

Brain Waves Characteristics in Individuals with Obsessive-Compulsive Disorder: A Preliminary Study

Obsessive-compulsive disorder (OCD) is a mental illness causing patients to suffer from recurring undesirable thoughts (obsessions) conducting to do affairs repetitively (compulsions). Brain signals recorded by Electroencephalogram (EEG) can be analyzed in order to present a diagnostic procedure considering the localization approach. In this study, the signals acquired by EEG have been recorded from three groups; two case groups; patients with severe obsessive symptoms and patients with severe compulsive symptoms, and one healthy control group. Brain signal processing techniques have been applied on the signals emitted from frontal and parieto-occipital regions to discover the features leading to the best discrimination between case groups and healthy controls. In this regard, after preprocessing, the features of time and frequency domains presenting the significant meaningful relation were nominated for classification by linear discrimination analysis (LDA). Although the parieto-occipital region performed better in the diagnosis for both obsessive and compulsive groups, the features gained from the frontal cortex resulted in better discrimination for only the compulsive group. In addition, time domain features had a more significant influence in diagnosis rather than frequency domain for both case groups. The study presented particular characteristics of brain signals in two dimensions of OCD in specific brain regions leading to more accurate presurgical assessments in the studies between the affected brain regions and behavioral issues

A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy

Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. Background: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. Methods: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. Results: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. Conclusions: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy

Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach

Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed

ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques

Heart disease remains the main leading cause of death globally and around 50% of the patients died due to sudden cardiac death (SCD). Early detection and prediction of SCD have become an important topic of research and it is crucial for cardiac patient’s survival. Electrocardiography (ECG) has always been the first screening method for patient with cardiac complaints and it is proven as an important predictor of SCD. ECG parameters such as RR interval, QT duration, QRS complex curve, J-point elevation and T-wave alternan are found effective in differentiating normal and SCD subjects. The objectives of this paper are to give an overview of SCD and to analyze multiple important ECG-based SCD detection and prediction models in terms of processing techniques and performance wise. Detail discussions are made in four major stages of the models developed including ECG data, signal pre-processing and processing techniques as well as classification methods. Heart rate variability (HRV) is found as an important SCD predictor as it is widely used in detecting or predicting SCD. Studies showed the possibility of SCD to be detected as early as one hour prior to the event using linear and non-linear features of HRV. Currently, up to 3 hours of analysis has been carried out. However, the best prediction models are only able to detect SCD at 6 minutes before the event with acceptable accuracy of 92.77%. A few arguments and recommendation in terms of data preparation, processing and classification techniques, as well as utilizing photoplethysmography with ECG are pointed out in this paper so that future analysis can be done with better accuracy of SCD detection accuracy

The role of brain signal processing and neuronal modelling in epilepsy – a review

Epilepsy is a neurological disorder characterized by recurrent seizures due to spontaneous changes of chemical synaptic coupling within the central nervous system. Numerous studies have been done in order to increase the level of cognition in epilepsy. Electroencephalography (EEG) as a non-invasive technique with the ability of presenting potentials on the head surface due to neural activity is widely used in epilepsy studies. The signals have been analyzed by brain signal processing techniques which mainly are categorized in feature extraction, feature dimensionally reduction and classification. The limitations such as inapproachability to intracranial in vivo and few seizure occurrences during sampling led to investigate on a model of signals and neural activity. This paper reviews the fundamentals of epilepsy toward using brain signal processing and neuronal modeling in three major branches; detection, prediction and source localization. It resulted a rare number of investigations on seizure epilepsy prediction due to the lack of long-term epilepsy EEG recording ending to the seizure. Subsequently, this review paper suggests to consider brain signal processing techniques in sub-branches of epilepsy detection; status, type, markers and surface localization, whilst it plays a remarkable role targeting to the source localization by neuronal modeling

Bio-FET Sensor Interface Module for COVID-19 Monitoring Using IoT

Rapid transmission of the coronavirus disease via droplets and particles has led to a global pandemic. Expeditious detection of SARS-Cov-2 RNA in the environment is attainable by using Bio-FET sensors. This work proposes a Bio-FET sensor interface module with IoT implementation to amplify signals from a Bio-FET for SARS-Cov-2 detection and monitoring. The sensor interface module was programmed to read the signals using a micro-controller and process information to determine the presence of SARS-Cov-2. The proposed Bio-FET sensor interface module was also set to transmit data to the Cloud via W-Fi to be stored and displayed on a dashboard. The prototype Bio-FET sensor interface module was simulated in PSpice for signal amplification, and hardware implementation has been done by using low-cost components for data transmission to the Cloud. The hardware consists of an AD620 instrumentation amplifier module, voltage sensor module, Neo-6m GPS sensor module, an OLED display, and an ESP8266-32 bit micro-controller. The results of both the simulation and the hardware implementation are similar. The emulated negative and positive Bio-FET signal outputs were successfully amplified from 15.9mV and 45.8mV to 1.59V and 4.58V, respectively, using an AD620 instrumentation amplifier. The gathered location, time, date, output voltage, and SARS-Cov-2 presence results were successfully stored and displayed on the Cloud dashboard.&nbsp