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

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

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

Development of low cost and robust nursing calling device for hospitalized patients

Patient care is considered in top priority for all clinics and hospitals. Hence it is necessary to provide a dedicated line of a mediator between nurse and patient to provide excellent patient safety and to minimize the medical errors. Till date, traditional nursing calling system was used for this purpose which limits the patient information until the nurse’s chamber and outcomes of the system depended on the nurse that sometimes fail due to unavailability or shortage of manpower. It may lead to patient death too. Therefore, this study presents a low cost and robust nursing calling system. The proposed system consists of five parts: 1) patient switch, 2) acknowledgment switch, 3) nurse center station board, 4) administrator board and 5) control board. Whenever a patient pressed the button, the signal enabled the patient’s room and bed number along with demographic details in nurse chamber for two minutes. If unattended by nurses, it passes the signal to the administrator or physician room of the hospital for the further action. In addition, it is also capable of measuring the response time delivered by the nurse to the patient which is useful for the hospital management. Specially, it was designed for those patients who have been shifted from the ICU to General Ward for the better care of them. The total cost of the device is 15$ for each bed which shows the cost effectiveness of the device. Our developed device a step ahead of existing technology by technology for the ease of patients

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%)