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

Filter � GA Based Approach to Feature Selection for Classification

This paper presents a new approach to select reduced number of features in databases. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as and can confuse the process of classification. The proposed method applies filter attribute measure and binary coded Genetic Algorithm to select a small subset of features. The importance of these features is judged by applying K-nearest neighbor (KNN) method of classification. The best reduced subset of features which has high classification accuracy on given databases is adopted. The classification accuracy obtained by proposed method is compared with that reported recently in publications on twenty eight databases. It is noted that proposed method performs satisfactory on these databases and achieves higher classification accuracy but with smaller number of features

A novel arrhythmia classification method based on ant colony optimization

Bu çalışmada, Elektrokardiyogram (EKG) işaretlerinde ortaya çıkan aritmilerin Karınca Koloni Optimizasyon (KKO) temelli kümeleme teknikleri ile sınıflanması gerçekleştirilmiştir. Kullanılan EKG işaretleri MIT-BIH aritmi veritabanından alınmıştır. Yapılan uygulamada zaman uzayı öznitelikleri ve frekans uzayında Ayrık Dalgacık Dönüşüm (ADD) öznitelikleri analiz edilmiştir. ADD öznitelik sayısı zaman uzayındaki öznitelik sayısıyla karşılaştırıldığında oldukça fazla olduğundan Temel Bileşen Analizi (TBA) vasıtasıyla farklı bir uzaya dönüştürülerek boyutları azaltılmış ve en yüksek enerjiye sahip öznitelikler seçilmiştir. Frekans uzayında seçilen bu öznitelikler zaman uzayında seçilen öznitelikler ile birleştirilerek sınıflayıcıya verilecek toplam giriş vektörü elde edilmiştir. Zaman uzayında, frekans uzayında ve her ikisi birlikte olacak şekilde farklı öğrenme ve test kümeleri oluşturularak sonuçları mukayese edilmiştir. KKO temelli sınıflayıcının başarımını test etmek ve doğrulamak için yapılan çalışmaya paralel olarak Kohonen ağı ve Geri Yayılımlı Yapay Sinir Ağı (GYYSA) sistemi geliştirilmiştir. Geliştirilen algoritmaların testi için MIT-BIH veritabanında 360 Hz ile LMII kanalından örneklenen 6 farklı ve önemli aritmi sınıfı kullanılmıştır. Normal sinus ritmi, erken karıncık atımları (“premature ventricular contraction”, PVC), erken kulakçık atımları (“atrial premature contraction”, APC), sağ dal blok (“right bundle branch block”, RBBB), karıncık füzyonu (“ventricular fusion”, F) ve füzyon (“fusion”, f). Zaman uzayındaki ve frekans uzayındaki özniteliklerin birlikte kullanılmasının başarımı artırdığı görülmüştür.   Anahtar Kelimeler: Elektrokardiyogram, aritmi sınıflama, temel bileşen analizi, karınca koloni optimizasyonu.Electrocardiogram is widely used in cardiology since it consists of effective, simple, noninvasive, low-cost procedures for the diagnosis of cardiovascular. The state of cardiac heart is generally reflected in the shape of ECG waveform and heart rate.  Cardiac arrhythmia is any of a group of conditions in which the electrical activity of the heart is irregular or is faster or slower than normal. There are many inherited conditions and heart diseases that can cause ECG signals arrhythmia occurrence. The one of the most difficult problem faced by today's automatic ECG analysis is the large variation in the morphologies of ECG waveforms. The ECG waveforms may differ for the same patient to such extend that they are unlike to each other and at the same time alike for different types of beats. In last decades, cluster analysis, has been combined with other techniques, has been used to overcome these difficulties in many areas of ECG processing, such as classification of ECG arrhythmias, ECG feature selection, ECG character points detection, classification of ECG morphology. In this paper, Ant Colony Optimization (ACO) based clustering analysis of ECG arrhythmias taken from the MIT-BIH Arrhythmia Database is proposed. Both time domain and discrete wavelet transform based frequency domain features are used in the analysis. Mainly, three operations must be implemented for computer aided classification: 1) preprocessing 2) feature extraction, 3) classification. Preprocessing stage contains filtering, baseline detection and correction,  and normalization. Feature extraction is the process in order to determine the different coefficients for describing the ECG waveform. In this work, in order to extract the best features that will represent the structure of ECG signals, methods based on time domain features and Discrete Wavelet transforms are used. Since the numbers of wavelet coefficients are huge amount as compared to the time domain parameters, principle component analysis based compression is applied on them in order to decrease their number to the same level of time domain features. Then, the reduced number of frequency parameters is combined with the time domain features in order to get the total feature sets. Different types of feature sets are tried and the classification results are compared. These are; rare time domain feature set, rare frequency domain feature set and the mixture of them. Third stage is the classification of ECG signals according to their feature sets. In this thesis, Ant Colony Optimization (ACO) based clustering analysis of 6 different type arrhythmias taken from the MIT-BIH Arrhythmia Database is proposed. These are normal sinus rhythm, premature ventricular contraction, atrial premature contraction, right bundle branch block, ventricular fusion and fusion. Both time domain and wavelet based frequency domain features are used in the analysis. In ACO method, the k-nearest neighborhood classification algorithm is used to determine the ECG signals class.  Kohonen and back propagation neural network algorithms are developed in parallel to verify and measure the ACO classifier's success. The ACO based classification method is depends on a learning method that can be called semi-supervised. In the stage of construction of intra-class cluster, the label of input sample's class is known but the clusters are determined by algorithm. Therefore the proposed system's success is compared with the supervised back propagation neural network and unsupervised Kohonen networks. Because the back propagation neural network is one of the most used methods in literature the comparison with ACO method is important also. Comparisons results indicate that the mixture feature set give a better success for classification. Keywords: Electrocardiogram, arrhythmia classification, principal component analysis, ant colony optimization

Modified and Ensemble Intelligent Water Drop Algorithms and Their Applications

1.1 Introduction Optimization is a process that concerns with finding the best solution of a given problem from among the possible solutions within an affordable time and cost (Weise et al., 2009). The first step in the optimization process is formulating the optimization problem through an objective function and a set of constrains that encompass the problem search space (ie, regions of feasible solutions). Every alternative (ie, solution) is represented by a set of decision variables. Each decision variable has a domain, which is a representation of the set of all possible values that the decision variable can take. The second step in optimization starts by utilizing an optimization method (ie, search method) to find the best candidate solutions. Candidate solution has a configuration of decision variables that satisfies the set of problem constrains, and that maximizes or minimizes the objective function (Boussaid et al., 2013). It converges to the optimal solution (ie, local or global optimal solution) by reaching the optimal values of the decision variables. Figure 1.1 depicts a 3D-fitness landscape of an optimization problem. It shows the concept of the local and global optima, where the local optimal solution is not necessarily the same as the global one (Weise et al., 2009). Optimization can be applied to many real-world problems in various domains. As an example, mathematicians apply optimization methods to identify the best outcome pertaining to some mathematical functions within a range of variables (Vesterstrom and Thomsen, 2004). In the presence of conflicting criteria, engineers use optimization methods t

An ensemble of intelligent water drop algorithm for feature selection optimization problem

Master River Multiple Creeks Intelligent Water Drops (MRMC-IWD) is an ensemble model of the intelligent water drop, whereby a divide-and-conquer strategy is utilized to improve the search process. In this paper, the potential of the MRMC-IWD using real-world optimization problems related to feature selection and classification tasks is assessed. An experimental study on a number of publicly available benchmark data sets and two real-world problems, namely human motion detection and motor fault detection, are conducted. Comparative studies pertaining to the features reduction and classification accuracies using different evaluation techniques (consistency-based, CFS, and FRFS) and classifiers (i.e., C4.5, VQNN, and SVM) are conducted. The results ascertain the effectiveness of the MRMC-IWD in improving the performance of the original IWD algorithm as well as undertaking real-world optimization problems