Review of infrared carbon-dioxide sensors and capnogram features for developing asthma-monitoring device

Introduction: Asthma is one of the most common heterogeneous respiratory chronic diseases and fourteenth most imperative illness in the world in terms of duration and extent of disability. The existing method for early identification of asthma is based on health care provider’s physical assessment and spirometer or peak flow meter which is manual and unreliable if patients are non-cooperative. Therefore, capnography, which measured the respired carbon dioxide concentration, has been proposed as a patient independent method for the assessment of asthma. Aim: This study aims to critically review, investigate, and compare the specifications of different infrared CO2 sensors and capnogram features to develop an asthma-monitoring device. Materials and Methods: A rigorous and extensive search was carried out on Google scholar, the Web of Science, PubMed, and Scopus and several index terms (CO2 sensor, infrared sensor, CO2 measurement, asthma detection, capnograph, and capnogram) were employed to identify appropriate CO2 sensors, technology, and capnogram features to develop asthma monitoring device. Results: The review revealed that the COMET CO2 sensor is the most suitable and reliable for developing a capnograph device owing to its weight (7 g), output range (0-99 mmHg), warm-up time (2-15 s), and response time (0.028 s). Furthermore, slope and time-frequency components measured from alveolar phase and complete breath cycle respectively are found the most significant features to screen asthma severity level. Further, the effects of pressure and temperature on CO2 values were tested using Proteus software. Finding reveals that the CO2 values changed drastically from 17,835.19 parts per million (ppm) to 86,321.29 ppm as the pressure changed from 16.53 kPa to 81.53 kPa at a constant temperature (25°C). With a change in temperature from 25 to 27°C, the CO2 values were found to change from 16,812.19 ppm to 17,249.13 ppm at a constant pressure (16.53 kPa). Based on the review, a CO2 measurement device using COMET equivalent CO2 sensor was developed. Conclusion: The developed device is capable of the assessment of cardiorespiratory condition instead of asthma severity level due to lack of significant capnogram features, which still remains to be integrated into the device

Fetal movements recording system using accelerometer sensor

One of the compelling challenges in modern obstetrics is the monitoring fetal wellbeing. Physicians are gradually becoming cognizant of the relationship between fetal activity, movement, welfare, and future developmental progress. Previous works have developed few accelerometer-based systems to tackle issues related to ultrasound measurement, the provision of remote s1pport and self-managed monitoring of fetal movement during pregnancy. Though, many research questions on the optimal setup in terms of body-worn accelerometers, as well as signal processing and machine learning techniques used to detect fetal movement, are still open. In this work, a new fetal movement system recorder has been proposed. The proposed system has six accelerometer sensors and ARDUINO microcontroller. The device which is interfaced with the MATLAB signal process tool has been designed to record, display and store relevant sets of fetal movements. The sensors are to be placed on the maternal abdomen to record and process physical signals originating from the fetal. Comparison of data recorded from fetal movements with ultrasound and maternal perception technique gave the following results. An accuracy of 59.78%, 85.87%,and 97.83% was achieved using the maternal perception technique, fetal movement recording system, and ultrasound respectively. The findings show that the proposed fetal movement recording system has a better accuracy rate than maternal perception technique, and can be compared with ultrasound

Quantitative Comparison of Time Frequency Distribution for Heart Rate Variability Using Performance Measure

Heart Rate Variability (HRV) has been proposed as a promising non-invasive method to assess Autonomic Nervous System (ANS). The recent trend of analysing HRV, which is a non-stationary signal is using the Time Frequency (TF) analysis such as Time Frequency Distribution (TFD). However, the use of TFD is different for every application, therefore, comparison of TFD performance needs to be carried out to select the suitable TFD. The comparisons performed by previous studies were limited to visual comparison which is very subjective and could lead to error. Therefore, this paper presents an objective quantitative comparison using performance measure, M to select the suitable TFD that characterises HRV response during an Autonomic Function Test (AFT). The investigated TFDs are the Wigner Ville (WVD), Smoothed Pseudo Wigner Ville (SPWVD), Choi William (CWD), Spectrogram (SP), and recently introduced Modified B-Distribution (MBD). From the results, we conclude that MBD and SPWVD demonstrated the highest value of performance measure M, with

Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience

Adverse childhood experiences have been suggested to cause changes in physiological processes and can determine the magnitude of the stress response which might have a significant impact on health later in life. To detect the stress response, biomarkers that represent both the Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenal (HPA) axis are proposed. Among the available biomarkers, Heart Rate Variability (HRV) has been proven as a powerful biomarker that represents ANS. Meanwhile, salivary cortisol has been suggested as a biomarker that reflects the HPA axis. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analyzed separately. Therefore, the objective of this study is to propose a fusion of ANS and HPA axis biomarkers in order to classify the stress response based on adverse childhood experience. Electrocardiograph, blood pressure (BP), pulse rate (PR), and salivary cortisol (SCort) measures were collected from 23 healthy participants; 11 participants had adverse childhood experience while the remaining 12 acted as the no adversity control group. HRV was then computed from the ECG and the HRV features were extracted. Next, the selected HRV features were combined with the other biomarkers using Euclidean distance (ed) and serial fusion, and the performance of the fused features was compared using Support Vector Machine. From the result, HRV-SCort using Euclidean distance achieved the most satisfactory performance with 80.0% accuracy, 83.3% sensitivity, and 78.3% specificity. Furthermore, the performance of the stress response classification of the fused biomarker, HRV-SCort, outperformed that of the single biomarkers: HRV (61% Accuracy), Cort (59.4% Accuracy), BP (78.3% accuracy), and PR (53.3% accuracy). From this study, it was proven that the fused biomarkers that represent both ANS and HPA (HRV-SCort) able to demonstrate a better classification performance in discriminating the stress response. Furthermore, a new approach for classification of stress response using Euclidean distance and SVM named as ed-SVM was proven to be an effective method for the HRV-SCort in classifying the stress response from PASAT. The robustness of this method is crucial in contributing to the effectiveness of the stress response measures and could further be used as an indicator for future health

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity

Finger Movement Recognition based on Muscle Synergy using Electromyogram

Motor functions of human hand during daily living activities involve multiple finger movements, which has not yet been fully explored for electromyogram (EMG) based prosthesis control. This paper presents a framework based on forearm muscle synergy for recognition of finger movement using four channel EMG. With five normal-limbed subjects, synergy of four forearm muscles was estimated for five finger movements through non-negative matrix factorization of EMG feature. Using leave-one-patient-out cross-validation, radial basis function support vector machine was implemented for recognition of finger movements. The framework exhibited an average recognition rate of 97%. This study offers feasibility of a finger movement recognition framework based on the inherent physiological mechanism of muscle synergy, which has potential for dexterous finger movement control in prosthetic hand

Real-time human respiration carbon dioxide measurement device for cardiorespiratory assessment

The development of a human respiration carbon dioxide (CO2) measurement device to evaluate cardiorespiratory status inside and outside a hospital setting has proven to be a challenging area of research over the few last decades. Hence, we report a real-time, user operable CO2 measurement device using an infrared CO2 sensor (Arduino Mega2560) and a thin film transistor (TFT, 3.5″), incorporated with low pass (cut-off frequency, 10 Hz) and moving average (span, 8) filters. The proposed device measures features such as partial end-tidal carbon dioxide (EtCO2), respiratory rate (RR), inspired carbon dioxide (ICO2), and a newly proposed feature - Hjorth activity - that annotates data with the date and time from a real-time clock, and is stored onto a secure digital (SD) card. Further, it was tested on 22 healthy subjects and the performance (reliability, validity and relationship) of each feature was established using (1) an intraclass correlation coefficient (ICC), (2) standard error measurement (SEM), (3) smallest detectable difference (SDD), (4) Bland-Altman plot, and (5) Pearson's correlation (r). The SEM, SDD, and ICC values for inter- and intra-rater reliability were less than 5% and more than 0.8, respectively. Further, the Bland-Altman plot demonstrates that mean differences ±standard deviations for a set limit were 0.30 ±0.77 mmHg, -0.34 ±1.41 mmHg and 0.21 ±0.64 breath per minute (bpm) for CO2, EtCO2 and RR. The findings revealed that the developed device is highly reliable, providing valid measurements for CO2, EtCO2, ICO2 and RR, and can be used in clinical settings for cardiorespiratory assessment. This research also demonstrates that EtCO2 and RR (r, -0.696) are negatively correlated while EtCO2 and activity (r, 0.846) are positively correlated. Thus, simultaneous measurement of these features may possibly assist physicians in understanding the subject's cardiopulmonary status. In future, the proposed device will be tested with asthmatic patients for use as an early screening tool outside a hospital setting

Two channel data acquisition system for heart sound segmentation algorithm based on instantaneous energy of electrocardiogram

This paper presents the hardware design of 2- channel data acquisition system for heart sound and Electrocardiogram (ECG) to capture the heart sound and ECG simultaneously from patients; and software algorithm to detect the first heart sound (S1) and second heart sound(S2). The algorithm utilizes Instantaneous Energy of ECG to estimate the presence of S1 and S2. Thus, heart sound segmentation can be done as it is essential in the automatic diagnosis of heart sounds. The Instantaneous Energy of ECG is performed to verify the occurrence of S1 and S2 as it is widely accepted pathologically that Phonocardiogram (PCG) and Electrocardiogram (ECG) are two noninvasive source of information depicting the cardiac activity [6]. The hardware consists of instrumentation amplifier, filter, isolation amplifier for each channel, multiplexer, Analogue to Digital Converter (ADC) and microcontroller 68HC11 to control and handle communication protocols with PC. The algorithm was tested for 210 cardiac cycles of heart sound and ECG recorded from patients from normal and abnormal simultaneously

Comparative analysis of preprocessing techniques for quantification of heart rate variability

In this paper, a comparative analysis of preprocessing techniques for quantification of heart rate variability (HRV) were performed. These preprocessing techniques are used to transform the Electrocardiogram (ECG) to HRV so that appropriate for spectral and non linear analysis. A number of preprocessing techniques were investigated in this study. In order to evaluate the performance of the preprocessing methods, the differences between the frequency spectrum of the HRV were measured by contrasting the merit indices. Among the preprocessing techniques studied, the result indicate that the utilization of heart rate values instead of heart period values in the derivation of HRV results in more accurate spectrum. Furthermore, the result support that the preprocessing technique based on the convolution of inverse interval values with the rectangular window and the cubic interpolation of inverse interval values are efficient methods for quantification of HRV