다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Digital processing of speech signals

QUITE apart from the academic consideration that vital and medical statistics now form an obligatory part of the education of students seeking the University of London's diploma in public health, the demand for information about the methods of vital and medical statistics is increasing. The most casual reader of the newspapers is now aware that population problems are of serious practical importance and that the publications of the General Register Office cannot be ignored. It is scarcely an exaggeration to say that the recently issued preliminary report on the census of 1931 is one of the most sensational documents which has appeared for years, and that he who reads it intelligently will understand what is meant by saying that civilisation is in the melting pot. An Introduction to Medical Statistics. By Hilda M. Woods William T. Russell. Pp. x + 125. (London: P. S. King and Son, Ltd., 1931.) 7s. 6d.

/pdf/an-introduction-to-medical-statistics-58gm2cuxfv.pdf

An Introduction to Medical Statistics

Discrimination of types of seizure using the Electroencephalogram (EEG) signal has always been a challenging task due to minuscule differences among different types of seizures. In this regard, deep learning (DL) which has already evidenced notable performance in image recognition could be suitable. However, a few attempts have been made so far in this regard mainly by constructing 2D input images for DL from 1D EEG signals directly using various techniques. Besides, the quality of the generated images has not been verified. Therefore, in this work, 2D images for the DL pipeline have been generated from brain rhythms, which already displayed remarkable performance in analyzing various brain activities. For this purpose, the Markov transition field transformation technique has been employed for 2D image construction by preserving statistical dynamics characteristics of EEG signals, which are very important during the discrimination of different types of seizures. And, a convolution neural network (CNN) has been used for classification. Further, the quality of the 2D image along with appropriate brain rhythms have also been investigated. For experimental validation, EEG recordings of six different types of seizure that are provided by the Temple University EEG dataset (TUH v1.5.2) has been taken into account. The proposed method has achieved the highest classification accuracy and weighted ${F}1$ -score up to 91.1% and 91.0% respectively. Further analysis shows that higher image resolution can provide the best classification accuracy. In addition, the $\delta $ rhythm has been found the most suitable in seizure type classification. In a comparative study, the proposed idea demonstrated its superiority by displaying the uppermost classification performance.

https://ieeexplore.ieee.org/ielx7/9552935/9775186/09870676.pdf

Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning

Vowel nasalization is present in almost every Indic languages. Detection of vowel nasalization can enhance the accuracy of Automatic Speech Recognition (ASR) systems designed for Indian languages. It also provides significant clinical information about the vocal tract. In pursuit of developing some acoustic parameters for detection of nasalized vowels, most researchers have extensively analyzed its spectral domain characteristics. In this work, we have used an inverse filtering based technique to develop a novel feature, which represents the amount of nasalization present in a vowel. The invariability of nasal filter for different nasalized vowels and addition of oral and nasal speech after radiation has been exploited to find out this feature. As the feature gives information about the amount of nasalization, this can be used for detection of vowel nasalization as well as for clinical purposes. Statistical analysis of the feature has been done in this work. The statistical analysis shows that the feature has good separability for oral vowels and nasalized vowels.

Inverse Filtering Based Feature for Analysis of Vowel Nasalization

This article presents a phonocardiogram (PCG)-based heart valve disease (HVD) detection for primary healthcare units. In this work, we propose a stationary wavelet transform (SWT) decomposition, followed by an attention-based hierarchical long short-term memory (HLSTM) network for each subband to detect HVDs. Initially, the PCG signal is preprocessed and segmented into PCG segments. Then, each PCG segment is decomposed using SWT. We employ subband-specific HLSTM networks to utilize the temporal and relative temporal information present in each subband of the PCG segment for detecting HVDs. Then, the outputs of each subband-specific HLSTM are fed to their respective intra-subband attention layer for weighted aggregation to obtain the subband-specific representation. Furthermore, the inter-subband attention layer aggregates these subband-specific representation vectors to improve the detection of HVDs. The proposed method is tested and validated using two open-access databases. The Physionet Challenge 2016 database shows an overall sensitivity (OSe) of 97.96%, overall specificity (OSp) of 99.02%, and overall accuracy (OA) of 98.55%, using the proposed method for binary classification. Similarly, the heart sound (HS) murmur database shows impressive classwise performance measures and an OA of 99.47%, using the proposed method for multiclass HVD classification. The proposed method’s impressive performance and generalization can help to detect HVD anomalies during preliminary checkups in healthcare units.

Automated Detection of Heart Valve Diseases Using Stationary Wavelet Transform and Attention-Based Hierarchical LSTM Network

The electrocardiogram (ECG) signal is unique for each person. Due to the physiological and geometrical differences of the heart, the morphological shape of the ECG signal changes for different persons. In this work, we have proposed a multiscale residual neural network (MS-ResNet) architecture to exploit the morphological shape of the ECG waveforms and their inter-relationship for biometric application. The proposed neural network model utilises three parallel convolutional branches with dilated kernels for capturing the multi-scale representation of the ECG signal. The proposed MS-ResNet model is evaluated using two publicly available ECG datasets, i.e., the UofTDB dataset and CYBHi dataset. The proposed model gives an identification accuracy of 87.98% and 94.18% for the UofTDB and CYBHi datasets, respectively.

A Multi-Scale Residual Neural Network for ECG Based Person Identification

In this work, a stressed (emotional) speech recognition system is designed based on Vector-Quantization (VQ) approach using Generalized Lloyd algorithm. Frequency, Amplitude and Phase features are extracted from the Sinusoidal model of speech and these parameters are used for characterization of stressed speech. The stressed conditions considered in this study are anger, happiness, compassion and neutral. Data for analysis and classification of stressed speech are recorded from thirty speakers; each speaker uttered five statements for each emotion in two different languages, English and Telugu (an Indian language). The features of stressed speech signals are compared with the features of neutral speech signals. From the results, it has been observed that the sinusoidal model features can be used successfully for classification of stressed speech. A total of 320 stressed speech data files are used for training and 280 stressed speech data files are used for testing. The performance of the VQ classifier with sinusoidal model features has been tested under normal and noisy conditions. A maximum recognition of 92.8% is achieved with frequency features as the input to the VQ classifier.

Samarendra Dandapat

Papers

Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning

Inverse Filtering Based Feature for Analysis of Vowel Nasalization

Automated Detection of Heart Valve Diseases Using Stationary Wavelet Transform and Attention-Based Hierarchical LSTM Network

A Multi-Scale Residual Neural Network for ECG Based Person Identification

Vector Quantization Approach to Classification of Stressed Speech