Dipali Bansal

Design of Simulink Model to denoise ECG signal using various IIR & FIR filters

Electrocardiogram ECG signal is usually corrupted by several artifacts and these must be removed before diagnosis. This paper therefore presents the design of various FIR filters like Kaiser, Rectangular, Hamming, Hanning, Gaussian and Bartllet window techniques and FIR Equiripple Filter. IIR filters like Butterworth, Chebychev I & II and Elliptic Filters are also explored to remove the artifacts in ECG signal. To verify the usability of designed filters, noisy ECG signal is generated by adding random noise, white noise and 50 Hz interference (hum) to the 100m.mat ECG data sample taken from MIT-BIH database. Signal to noise ratio (SNR) is calculated to compare the performance of different filtering methods using Simulink Model. Results obtained show that FIR Equiripple filter gives a SNR of 7.71 for a 3rd order filter and IIR Elliptic filter has SNR of for first order filter and are therefore recommended.

Algorithm for online detection of HRV from coherent ECG and carotid pulse wave

Measurement and analysis of Heart Rate Variability (HRV) is progressively gaining attention as it enables a clinical and non-invasive system to assess human physiology. QRS complex of an ECG forms the basis of HRV, but literature also establishes its relation with carotid artery pulsation. HRV analysis is either simulated or done offline using ECG database. An attempt has been made here to develop a computer-based dual channel acquisition system for online detection of HRV from real time simultaneously acquired ECG and carotid waveforms of human subjects. An algorithm is developed in MATLAB to acquire and filter both signals simultaneously. Algorithm to find online threshold-based maxima and difference between the adjacent RR peaks is also made and tested to calculate time domain HRV parameters. Close correlation is found in HRV data calculated from ECG and carotid signal. Carotid pulsation is easy to acquire using simple electronics and thus can be potentially used for HRV analysis compared to ECG acquisition. The measurement unit developed is simple and relatively cost effective.

Design of 50 Hz notch filter circuits for better detection of online ECG

Technological evolution in bio-signal processing has enabled computer-based analysis of ECG which is an adjunct to the doctor in terms of feature extraction and diagnosis. However, acquisition and reliable parameter extraction of ECG signals using a computer based system is extremely susceptible to power line interference. 50 Hz active analogue notch filter is used to trim down hum in such applications. An attempt has been made to design various 50 Hz active notch-filter topologies in P-spice. Based on their amplitude and phase response, add-on notch filter hardware is developed and used with real-time computer based ECG system. The amplifier system includes biosensors, cascaded amplifiers, right leg drive circuits, active filters and 50 Hz notch filters. Analogue output of amplifier system is interfaced to the computer for recording and analysis. Thus a computer based compact and enhanced signal acquisition system is developed that yields online ECG of greatest clinical use.

Primitive Components of Reversible Logic Synthesis

Reversible logic has various applications in fields of computer graphics, optical information processing, quantum computing, DNA computing, ultra low power CMOS design and communication. As our day to day life is demanding more and more portable electronic devices, challenging focus on technology is demanding great system performance without any compromise in power consumption. It is obvious to find tradeoff between processing power and heat generation. As decreased processing speed leads to reduced power consumption but obviously compromise in performance is not acceptable for sophisticated applications. Thus power consumption is a prime target now days. Needless to say, researchers will now look at reversible logic in this vein. Primitive component of reversible logic synthesis are reversible logic gates. Thus it is very important for a new researcher to look into extensive literature survey of reversible logic gates. Many papers have been reported with review of reversible logic gates. This paper aims on updates in reversible logic gates which are stepping stones in design and synthesis of any complex reversible logic based synthesis.

Hybrid ECG signal compression system: A step towards efficient tele-cardiology

Electrocardiogram (ECG) is a primary clinical diagnostic tool for detection of cardiac arrhythmias. As ECG signals are generally acquired over longer time periods at extremely high resolution and thus are highly data intensive. This leads to the requirement of large storage space for database construction and more transmission bandwidth for remote ECG signal analysis, respectively. Successive ECG beats and sample values however, show some redundancy along with the information content. By removing this redundancy, ECG signal compression can be achieved. This paper comprises implementation of hybrid ECG signal compression system based on frequency transformation and parameter extraction techniques. It uses discrete cosine transform (DCT) and Fast Fourier transform (FFT) to compress the ECG signal. This compressed ECG signal is embedded with corresponding heart rate information in order to obtain a high quality reconstructed signal required for accurate cardiac state diagnosis. The proposed algorithm is tested for compression of bradycardia and tachycardia ECG rhythms selected from MIT-BIH arrhythmia database and the performance is evaluated using compression ratio and percent root-mean-square difference (PRD). The high compression ratio, low reconstruction error and less computational complexity justify the efficiency of hybrid techniques in ECG signal compression and thus in telecardiology.

Design and development of BCI for online acquisition, monitoring and digital processing of EEG waveforms

Commercially available EEG acquisition units provide ability to acquire and view EEG signals in real time using their proprietary user interface software. Analysis of such EEG data is limited to the capabilities provided within these acquisition units, thereby making it necessary to process data in multi-paradigm computing environment. Offline uploading of the data into standard tools for further analysis may cause loss of information. This paper gives details of a simple and robust mechanism for online acquisition and real time processing of EEG signals in MATLAB without any loss of information, using custom developed API. Resulting wave decomposition into discrete samples and channels also reduce complexity in processing data. The designed system, using portable wireless Emotiv EEG neuroheadset, can easily be adopted for web-based remote monitoring of live EEG for applications in the field of mobile health. Moreover, developed BCI can be miniaturised and designed as SoC (System on Chip).

Automatic speech recognition using optimal selection of features based on hybrid ABC-PSO

Optimal Speech feature extraction which tries to acquire a parametric illustration of an input speech signal plays a vital role in the overall performance of an Automatic Speech Recognition (ASR) system. A good feature extraction technique along with feature selection algorithm should capture the important features of the signal and also should reject some irrelevant features. Feature selection is a critical task which can influence the performance of pattern classification and recognition system. In this paper, we have presented a hybrid evolutionary algorithm called Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) for optimal feature extraction and selection strategy. The proposed method consists of three stages, preprocessing, feature extraction and selection, and recognition. Initially, the preprocessing is done by wiener filter to reduce the noise level. Next, we extract eight type of statistical and acoustic features in the feature stage. The optimal set of extracted features is selected by using hybrid ABC-PSO algorithm. Finally, these optimized features are used for training the Support Vector Machine (SVM) classifier and based on these optimized features of the given input speech signal the corresponding text is displayed as the output. The proposed ASR is implemented on the MATLAB working platform and the experimental results show that overall performance of the system is high and the proposed hybrid algorithm is best suited for speech recognition.

A real-time acoustic signature-based fluid identification methodology for applications in the field of security and defence

Acoustic-based inspection methods, employed for on field screening of containers, hold high importance in the fields of security and defence. However, such methods are limited by expensive instrumentation, offline analysis and complexities related to transducer container physical coupling. Simple, non-destructive and extremely cost effective method based on acoustic resonance has been devised here for rapid online acquisition and analysis of acoustic signatures of fluids for their necessary identification and classification. The research, with two stages of experimentation, establishes reliability of the system in the first stage by using sweep excitation in fluids followed by acquiring acoustic signatures for such fluids in the second stage by using white noise for excitation. The setup uses two ceramic coated piezoelectric transducers interfaced with the computer through audio ports. Excellent consistency in the results, recorded with the help of a virtual spectrum analyzer, is achieved with average error amounting to 1.4%.

Design and implementation of secured bidirectional remote monitoring system for analysis of online live EEG feed

In this paper, a wireless network-based secured bidirectional remote monitoring system for analysis of online live EEG feed has been proposed. The technique makes use of a custom developed application programming interface plug-in to acquire live EEG feed directly in MATLAB on a host machine so as to build an On-Premise set-up for acquisition and processing of EEG data in real time. The online acquired EEG signals are then transmitted in real time over internet using secured virtual private network. Same EEG data can be viewed by multiple users simultaneously on smart devices connected from different geographical locations and over disparate network connectivity. The set-up includes a host computer with MATLAB, a commercially available Emotiv EEG neuro-headset, an android smart device and an iOS smart device. The future aim is to replicate set-up on public cloud SaaS offering and also move towards sophisticated user interfaces for handset applications.

Development of ocular motory movements-based online BCI model with multi-triggering capabilities for rehabilitative control

BCI technology, immensely used in the field of neuro-prosthetic control, permits neural activity alone to control external devices. However, such systems mostly suffer from induced latency owing to complex interfacing in their design, thereby compromising on vital real-time aspect. In this paper, ocular motory movements-based BCI system capable of triggering multiple controls by acquiring and processing online EEG data directly in MATLAB using a custom developed Application Programming Interface (API) is proposed. Based on extracted linear temporal data statistics, a threshold algorithm is designed to detect thoughtful single and double eye blinks in order to generate control signals for playing two different audio files. The rigorous online testing further undertaken verifies the feasibility of developed BCI system in practical applications and also exhibits excellent accuracy. The simple experimental set-up uses an inexpensive commercially available Emotiv EEG neuro-headset as the data acquisition unit. The developed interface can further be miniaturised and designed as system on chip.