Compressive Sensing with Low-Power Transfer and Accurate Reconstruction of EEG Signals

Tele-monitoring of EEG in WBAN is essential as EEG is the most powerful physiological parameters to diagnose any neurological disorder. Generally, EEG signal needs to record for longer periods which results in a large volume of data leading to huge storage and communication bandwidth requirements in WBAN. Moreover, WBAN sensor nodes are battery operated which consumes lots of energy. The aim of this research is, therefore, low power transmission of EEG signal over WBAN and its accurate reconstruction at the receiver to enable continuous online-monitoring of EEG and real time feedback to the patients from the medical experts. To reduce data rate and consequently reduce power consumption, compressive sensing (CS) may be employed prior to transmission. Nonetheless, for EEG signals, the accuracy of reconstruction of the signal with CS depends on a suitable dictionary in which the signal is sparse. As the EEG signal is not sparse in either time or frequency domain, identifying an appropriate dictionary is paramount. There are a plethora of choices for the dictionary to be used. Wavelet bases are of interest due to the availability of associated systems and methods. However, the attributes of wavelet bases that can lead to good quality of reconstruction are not well understood. For the first time in this study, it is demonstrated that in selecting wavelet dictionaries, the incoherence with the sensing matrix and the number of vanishing moments of the dictionary should be considered at the same time. In this research, a framework is proposed for the selection of an appropriate wavelet dictionary for EEG signal which is used in tandem with sparse binary matrix (SBM) as the sensing matrix and ST-SBL method as the reconstruction algorithm. Beylkin (highly incoherent with SBM and relatively high number of vanishing moments) is identified as the best dictionary to be used amongst the dictionaries are evaluated in this thesis. The power requirements for the proposed framework are also quantified using a power model. The outcomes will assist to realize the computational complexity and online implementation requirements of CS for transmitting EEG in WBAN. The proposed approach facilitates the energy savings budget well into the microwatts range, ensuring a significant savings of battery life and overall system’s power. The study is intended to create a strong base for the use of EEG in the high-accuracy and low-power based biomedical applications in WBAN

VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network

Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Non-invasive fetal monitoring: a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size mu and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.Web of Science175art. no. 115

Non-invasive Detection and Compression of Fetal Electrocardiogram

Noninvasive detection of fetal electrocardiogram (FECG) from abdominal ECG recordings is highly dependent on typical statistical signal processing techniques such as independent component analysis (ICA), adaptive noise filtering, and multichannel blind deconvolution. In contrast to the previous multichannel FECG extraction methods, several recent schemes for single‐channel FECG extraction such as the extended Kalman filter (EKF), extended Kalman smoother (EKS), template subtraction (TS), and support vector regression (SVR) for detecting R waves on ECG, are evaluated via the quantitative metrics such as sensitivity (SE), positive predictive value (PPV), F‐score, detection error rate (DER), and range of accuracy. A correlation predictor that combines with multivariable gray model (GM) is also proposed for sequential ECG data compression, which displays better percent root mean-square difference (PRD) than those of Sabah’s scheme for fixed and predicted compression ratio (CR). Automatic calculation on fetal heart rate (FHR) on the reconstructed FECG from mixed signals of abdominal ECG recordings is also experimented with sample synthetic ECG data. Sample data on FHR and T/QRS for both physiological case and pathological case are simulated in a 10-min time sequence

A phonocardiographic-based fiber-optic sensor and adaptive filtering system for noninvasive continuous fetal heart rate monitoring

This paper focuses on the design, realization, and verification of a novel phonocardiographic-based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.Web of Science174art. no. 89

Learning-Based Hardware Design for Data Acquisition Systems

This multidisciplinary research work aims to investigate the optimized information extraction from signals or data volumes and to develop tailored hardware implementations that trade-off the complexity of data acquisition with that of data processing, conceptually allowing radically new device designs. The mathematical results in classical Compressive Sampling (CS) support the paradigm of Analog-to-Information Conversion (AIC) as a replacement for conventional ADC technologies. The AICs simultaneously perform data acquisition and compression, seeking to directly sample signals for achieving specific tasks as opposed to acquiring a full signal only at the Nyquist rate to throw most of it away via compression. Our contention is that in order for CS to live up its name, both theory and practice must leverage concepts from learning. This work demonstrates our contention in hardware prototypes, with key trade-offs, for two different fields of application as edge and big-data computing. In the framework of edge-data computing, such as wearable and implantable ecosystems, the power budget is defined by the battery capacity, which generally limits the device performance and usability. This is more evident in very challenging field, such as medical monitoring, where high performance requirements are necessary for the device to process the information with high accuracy. Furthermore, in applications like implantable medical monitoring, the system performances have to merge the small area as well as the low-power requirements, in order to facilitate the implant bio-compatibility, avoiding the rejection from the human body. Based on our new mathematical foundations, we built different prototypes to get a neural signal acquisition chip that not only rigorously trades off its area, energy consumption, and the quality of its signal output, but also significantly outperforms the state-of-the-art in all aspects. In the framework of big-data and high-performance computation, such as in high-end servers application, the RF circuits meant to transmit data from chip-to-chip or chip-to-memory are defined by low power requirements, since the heat generated by the integrated circuits is partially distributed by the chip package. Hence, the overall system power budget is defined by its affordable cooling capacity. For this reason, application specific architectures and innovative techniques are used for low-power implementation. In this work, we have developed a single-ended multi-lane receiver for high speed I/O link in servers application. The receiver operates at 7 Gbps by learning inter-symbol interference and electromagnetic coupling noise in chip-to-chip communication systems. A learning-based approach allows a versatile receiver circuit which not only copes with large channel attenuation but also implements novel crosstalk reduction techniques, to allow single-ended multiple lines transmission, without sacrificing its overall bandwidth for a given area within the interconnect's data-path