Prenatal Abdominal Electrocardiogram Monitoring System using IoT in Cloud Environment: A MIMO approach

<p>The application of Internet of Things (IoT) for acquiring, analyzing and transmission of medical data is increasing in recent years. Especially in abdominal ECG processing the need is more. Since the fetal movements are random in the abdomen, a single electrode can’t be able to acquire the fetal ECG. So multi-electrodes are used to record the same. At the same time all electrodes will not provide continuous ECG signal due to the fetal movements. The temperature, pressure and heart rate of the mother also monitored for effective diagnosis. This options makes the design a multi-input structure. In existing methods, Multi-input multi-output options are not available. In addition to that the complexity increases if number of input increases. In conventional methods, the complete machine is available in the patient room. But here in this work the product is divided into three units, bedside unit, doctors unit and main server. The bedside unit is an ECG acquisition device developed using a multi-lead heart rate monitor, sensors and microcontroller. Zigbee is used to transmit the information from the patient bedside to doctors unit which makes it wireless. During the movement of the patient also the data can be viewed. The Multi-output data corresponds to fetal ECG, maternal ECG, heart rate, temperature, pressure. The IoT using raspberry pi module connects the doctors unit with the main server. The machine learning algorithms analyze the ECG data of all electrodes and sensor outputs. The multi-outputs are viewed in a Graphical User Interface (GUI). The integration of the system is conducted to construct a complete IoT-based ECG monitoring system and diagnosis in Cloud environment. </p>

Prenatal Abdominal Electrocardiogram Monitoring System using IoT in Cloud Environment: A MIMO approach

<p>The application of Internet of Things (IoT) for acquiring, analyzing and transmission of medical data is increasing in recent years. Especially in abdominal ECG processing the need is more. Since the fetal movements are random in the abdomen, a single electrode can’t be able to acquire the fetal ECG. So multi-electrodes are used to record the same. At the same time all electrodes will not provide continuous ECG signal due to the fetal movements. The temperature, pressure and heart rate of the mother also monitored for effective diagnosis. This options makes the design a multi-input structure. In existing methods, Multi-input multi-output options are not available. In addition to that the complexity increases if number of input increases. In conventional methods, the complete machine is available in the patient room. But here in this work the product is divided into three units, bedside unit, doctors unit and main server. The bedside unit is an ECG acquisition device developed using a multi-lead heart rate monitor, sensors and microcontroller. Zigbee is used to transmit the information from the patient bedside to doctors unit which makes it wireless. During the movement of the patient also the data can be viewed. The Multi-output data corresponds to fetal ECG, maternal ECG, heart rate, temperature, pressure. The IoT using raspberry pi module connects the doctors unit with the main server. The machine learning algorithms analyze the ECG data of all electrodes and sensor outputs. The multi-outputs are viewed in a Graphical User Interface (GUI). The integration of the system is conducted to construct a complete IoT-based ECG monitoring system and diagnosis in Cloud environment. </p>

Harnessing technology to enable all women mobility in labour and birth: feasibility of implementing beltless non-invasive fetal ECG applying the NASSS framework

Background A new wireless and beltless monitoring device utilising fetal and maternal electrocardiography (ECG) and uterine electromyography, known as ‘non-invasive fetal ECG’ (NIFECG) was registered for clinical use in Australia in 2018. The safety and reliability of NIFECG has been demonstrated in controlled settings for short periods during labour. As far as we are aware, at the time our study commenced, this was globally the first trial of such a device in an authentic clinical setting for the entire duration of a woman’s labour. Methods This study aimed to assess the feasibility of using NIFECG fetal monitoring for women undergoing continuous electronic fetal monitoring during labour and birth. Women were eligible to participate in the study if they were at 36 weeks gestation or greater with a singleton pregnancy, planning to give birth vaginally and with obstetric indications as per local protocol (NSW Health Fetal Heart Rate Monitoring Guideline GL2018_025. 2018) for continuous intrapartum fetal monitoring. Written informed consent was received from participating women in antenatal clinic prior to the onset of labour. This single site clinical feasibility study took place between January and July 2020 at the Royal Hospital for Women in Sydney, Australia. Quantitative and qualitative data were collected to inform the analysis of results using the NASSS (Non-adoption, Abandonment, Scale up, Spread and Sustainability) framework, a validated tool for analysing the implementation of new health technologies into clinical settings. Results Women responded positively about the comfort and freedom of movement afforded by the NIFECG. Midwives reported that when no loss of contact occurred, the device enabled them to focus less on the technology and more on supporting women’s physical and emotional needs during labour. Midwives and obstetricians noticed the benefits for women but expressed a need for greater certainty about the reliability of the signal. Conclusion The NIFECG device enables freedom of movement and positioning for labouring women and was well received by women and the majority of clinicians. Whilst measurement of the uterine activity was reliable, there was uncertainty for clinicians in relation to loss of contact of the fetal heart rate. If this can be ameliorated the device shows potential to be used as routinely as cardiotocography (CTG) for fetal monitoring. This is the first time the NASSS framework has been used to synthesise the implementation needs of a health technology in the care of women during labour and birth. Our findings contribute new knowledge about the determinants for implementation of a complex technology in a maternity care setting. Trial registration The Universal Trial Number is reU1111-1228-9845 and the Australian and New Zealand Clinical Trial Registration Number is 12619000293167p. Trial registration occurred on the 20 February, 2019. The trial protocol may be viewed at http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377027

Fractional Order Butterworth Filter for Fetal Electrocardiographic Signal Feature Extraction

The non-invasive Fetal Electrocardiogram (FECG) signal has become a significant method for monitoring the fetus's physiological conditions, extracted from the Abdominal Electrocardiogram (AECG) during pregnancy. The current techniques are limited during delivery for detecting and analyzing fECG. The non - intrusive fECG recorded from the mother's abdomen is contaminated by a variety of noise sources, can be a more challenging task for removing the maternal ECG. These contaminated noises have become a major challenge during the extraction of fetal ECG is managed by uni-modal technique. In this research, a new method based on the combination of Wavelet Transform (WT) and Fast Independent Component Analysis (FICA) algorithm approach to extract fECG from AECG recordings of the pregnant woman is proposed. Initially, preprocessing of a signal is done by applying a Fractional Order Butterworth Filter (FBWF). To select the Direct ECG signal which is characterized as a reference signal and the abdominal signal which is characterized as an input signal to the WT, the cross-correlation technique is used to find the signal with greater similarity among the available four abdominal signals. The model performance of the proposed method shows the most frequent similarity of fetal heartbeat rate present in the database can be evaluated through MAE and MAPE is 0.6 and 0.041209 respectively. Thus the proposed methodology of de-noising and separation of fECG signals will act as the predominant one and assist in understanding the nature of the delivery on further analysis.