Fetal ECG extraction from maternal abdominal ECG using neural network

FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signal from com-posite maternal abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be con-sidered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG

Fetal heart rate monitoring during pregnancy for assessing the well being of the fetus

Long-term fetal heart rate (FHR) monitoring is necessary to ensure that any FHR abnormality, which may appear at any time during pregnancy and labor, can be detected. An ambulatory electrocardiogram (ECG) recorder employing three abdominal surface electrodes has been developed towards achieving such a monitoring. The difficulties encountered in determining the FHR from the maternal abdominal signal are mainly the interference due to the electromyogram and motion artifact, and relatively small amplitude of the fetal ECG compared to that of the maternal. Thus improvement to existing abdominal signal processing algorithm is necessary to increase the percentage of successful monitoring. A real-time algorithm has been developed for the simultaneous measurement of the fetal and maternal heart rates from the abdominal signal. The algorithm is based on digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima. A filtering technique has been utilized in the proposed algorithm to extract the fetal signal from the maternal abdominal signal. This is an alternative to a previous method which subtracts the maternal complexes from the abdominal signal with a need to overcome the problem of matching a template to the complexes. The proposed algorithm is capable of continuous ambulatory FHR monitoring either off-line, by using recorded signals, or on-line by a clinician during antenatal examination. The performance of the algorithm has been evaluated of the heart rates tracing processed from the abdominal signals. The resulting average accuracy is 83% for the FHR detection. The detection of the FHR from the maternal abdominal signal by the developed algorithm has also been compared with a short-term monitoring commercial instrument IFM-500 for the assessment of the reliability of the algorithm. The performance achieved from the comparison shows non-significant differences of means, low error percentages and linear correlation coefficient. A portable system based on the developed algorithm has the potential for increased percentage of real-time FHR detection thus enabling successful long-term fetal monitoring

Two dimensional array based overlay network for reducing delay of peer-to-peer live video streaming

Live video streaming is very useful for many events. For example, it can be very useful to make an announcement in an area, which is effected by natural disaster. So it is also very important to provide the live video stream without any time delay if possible. The live video data is streaming usually in a tree-based overlay network or in a mesh-based overlay network. In case of departure of a peer with additional upload bandwidth, the overlay network becomes very vulnerable to churn. In this paper, a two dimensional array-based overlay network is proposed for streaming the live video stream data. As there is always a peer or a live video streaming server to upload the live video stream data, so the overlay network is very stable and very robust to churn. Peers are placed according to their upload and download bandwidth, which enhances the balance of load and performance. The overlay network utilizes the additional upload bandwidth of peers to minimize chunk delivery delay and to maximize balance of load. The procedure, which is used for distributing the additional upload bandwidth of the peers, distributes the additional upload bandwidth to the heterogeneous strength peers in a fair treat distribution approach and to the homogeneous strength peers in a uniform distribution approach. The proposed overlay network has been simulated by QualNet from Scalable Network Technologies and results are presented in this paper. Both maximum delay and average delay has decreased compared to Fast-Mesh overlay network. The percentage change in both of maximum and average delay time are below 30%, even though the number of nodes increases 10 times

Two dimensional array based overlay network for balancing load of peer-to-peer live video streaming

The live video data is streaming usually in a tree-based overlay network or in a mesh-based overlay network. In case of departure of a peer with additional upload bandwidth, the overlay network becomes very vulnerable to churn. In this paper, a two dimensional array-based overlay network is proposed for streaming the live video stream data. As there is always a peer or a live video streaming server to upload the live video stream data, so the overlay network is very stable and very robust to churn. Peers are placed according to their upload and download bandwidth, which enhances the balance of load and performance. The overlay network utilizes the additional upload bandwidth of peers to minimize chunk delivery delay and to maximize balance of load. The procedure, which is used for distributing the additional upload bandwidth of the peers, distributes the additional upload bandwidth to the heterogeneous strength peers in a fair treat distribution approach and to the homogeneous strength peers in a uniform distribution approach. The proposed overlay network has been simulated by Qualnet from Scalable Network Technologies and results are presented in this paper

Power supply power-supply interference in smart sensors-to-microntroller interface for biomedical signals

The effects of power-supply interference on direct sensor-to-microcontroller interfaces based on measuring the charging and discharging time of an RC circuit that includes the sensor is observed in this assignment. By analysing the RC circuit, it shows that the measurement can be corrupted because of the power-supply trigger noise. To reduce the effects another resistor has been used to get the acceptable measurement of the charging and discharging time. Finally, a new approach also proposed to reduce the effects of power-supply and from this proposed circuit it can be said that the circuit is working properly. A microcontroller during communication with external circuits (or sensors) is making use of the signal conditioning circuits, which are required for signals conversion or translations to squeeze the acquired signal into a desirable range easily acceptable for the microcontroller.The common measurement chain in data-acquisition systems has four basic blocks: sensor, signal conditioner, analog-to-digital converter (ADC), and microcontroller shown in Figure 21.1. This measurement chain can be simplified by using an oscillator circuit as signal conditioner [1-4], since its time-modulated output signal can be directly connected to the microcontroller without using an ADC [5] shown in Figure 21.2. The measurement chain can be further simplified by directly connecting the sensor to the microcontroller [6-8], without using either a signal conditioner or an ADC shown in Figure 21.3. Such an interface circuit generally relies on measuring the charging or discharging time of an RC circuit that includes the senso

Neural network classifier for hand motion detection from EMG signal

EMG signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virtual reality games and physical exercise equipments. Additionally, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This paper represents the detection of different predefined hand motions (left, right, up and down) using artificial neural network (ANN). A backpropagation (BP) network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. The conventional and most effective time and timefrequency based feature set is utilized for the training of neural network. The obtained results show that the designed network is able to recognize hand movements with satisfied classification efficiency in average of 88.4%. Furthermore, when the trained network tested on unknown data set, it successfully identify the movement types

Characteristics Analysis of Electrostatic Generator

Due to the demand of the modern era, now electricity has become an important factor in every sector in our life. Almost everywhere the electromagnetic induction technique is used to generate the electrical power. Once upon a time, the electrostatic induction technique was used to generate the electrical power. Unfortunately, there is little information about the mathematical modeling and characteristics of static electricity generators. A Wimshurst electrostatic generator of disk diameter 34.0 cm has been designed and experimentally study the characteristics to find the optimized condition for maximum output power. From the results, it has been found that the generator has maximum performance when the conductor segment length about 30% of the diameter of the disk. The machine performance has intensely depended on the total surface area of the conducting material and slightly relied on the number of sectors/segments

An approach of differential capacitor sensor for landslide monitoring

A lot of lives and properties are lost in every year all over the world, due to various geological catastrophes, landslide or land - slip is among one of them. Nowadays, both manual and electronic monitoring systems ar e used to predic t the landslide . The manual monitoring system is laborious, has many limitations and most of the cases it is not practicable. Conversely, most of the electronic systems are complex and expensive. Most of the natural calamities occur without prior notice as a result, it damages the monitoring instrument as well. The monitoring sensor system should be planned as a spread network with a simple positional identification device such as RFID; hence the system can send the real time data without interrupt. In addi tion, the network should have a self - recovery, self - directed operation and actual data transmission facility in a critical situation. A distributed node network needs a lot of sensors with complex structure and it is expensive too. This paper describes a s imple and low cost system which comprises an underground pretension cable with a capacitor gauge sensor attached at one end. A wireless sensor network has been proposed for a simple landslide monitoring system using RFID. The sensing node network can opera te by initializing mode, measuring mode and urgent mode. The system is able to select automatically any one of the operating mode depending on the situation , which makes it a robust and dynamic control of real time data transmission system. A mathematical model has been developed for the system and verified by simulation. The result shows that an early prediction of the landslide is possible by using the proposed system