Samarendra Dandapat's research while affiliated with Indian Institute of Technology Guwahati and other places

Publications (99)

Article
Background and Objective : The hypo-reflective Optical Coherence Tomography (OCT) imaging of the pseudo retinal layer strengthens the analysis of various retinal disorders, including Macular Edema (ME)/Diabetic Macular Edema (DME). The primary challenge in the automatic identification and analysis of ME cases is the presence of multiplicative speck...
Chapter
In clinical practice, continuous recording and monitoring of the standard 12-lead electrocardiogram (ECG) is often not feasible. The emerging technology and advancement to record the ECG signal without the help of the medical expert’s in-home care or ambulatory conditions with minimal complexity have become more common in recent times. We aim to de...
Article
Atrial fibrillation (AF) burden is defined as the percentage of time the patient is in AF rhythm during a certain monitoring period. The accurate AF burden estimation from the long-term electrocardiogram (ECG) recordings provides improved prognostic value compared to the traditional binary AF diagnosis (present or absent) using the snapshot ECG. Ho...
Preprint
This paper presents methods for detecting out-of-breath speech (OBS) under the shortness-of-breath condition due to physical load. It uses constant Q transform (CQT) for warping frequency spectrum non-linearly, which focuses on spectral saliencies of speech signal under the said condition. The existing works using deep neural networks (DNN) have sp...
Preprint
This paper presents methods for detecting out-of-breath speech (OBS) under the shortness-of-breath condition due to physical load. It uses constant Q transform (CQT) for warping frequency spectrum non-linearly, which focuses on spectral saliencies of speech signal under the said condition. The existing works using deep neural networks (DNN) have sp...
Article
In this work, a novel method is proposed for the assessment of a person’s physical fitness from out-of-breath speech using Gaussian posteriorgram. Based on physical fitness, we consider two categories of persons, physically-active and physically-non-active. A physically-active person is somebody who regularly does physical exercises like jogging, r...
Article
Electroencephalogram (EEG) based seizure types classification has not been addressed well, compared to seizure detection, which is very important for the diagnosis and prognosis of epileptic patients. The minuscule changes reflected in EEG signals among different seizure types make such tasks more challenging. Therefore, in this work, underlying fe...
Article
The early and accurate detection of congestive heart failure (CHF) using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. Existing approaches show limited detection accuracy as they fail to capture the temporal ECG dynamics. Also, these methods lack model transparency and are often difficult to interp...
Article
Full-text available
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...
Article
In compressed sensing (CS)-based magnetic resonance imaging (MRI), it is very challenging to maintain the diagnostic quality due to limited measurements. Diagnostically critical information, like fine anatomical details, edges, and boundaries are distorted due to the leakage of energy and artifacts during CS-reconstruction. In this paper, we have p...
Article
Early detection and diagnosis of heart valve diseases (HVDs) can prevent cardiac arrest. This work proposes a novel feature fusion method for detecting HVDs using a Phonocardiogram (PCG) signal. In the proposed method, first, the raw PCG signal is pre-processed. Then, two feature fusion models are proposed based on mel-frequency cepstral coefficien...
Article
The electrocardiogram (ECG) based biometric system has recently gained popularity. Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system. This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services. In this paper, w...
Article
The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and the subtle variations in the pathological ECG characteristics pose challenges in designing reliable automated methods. Existing methods mostly use single deep convolution...
Conference Paper
A convolution neural network (CNN) architecture has been designed to classify epileptic seizures based on two-dimensional (2D) images constructed from decomposed mono-components of electroencephalogram (EEG) signals. For the decomposition of EEG, Hilbert vibration decomposition (HVD) has been employed. In this work, four brain rhythms - delta, thet...
Conference Paper
classification of seizure types plays a crucial role in diagnosis and prognosis of epileptic patients which has not been addressed properly, while most of the works are surrounded by seizure detection only. However, in recent times, few works have been attempted on the classification of seizure types using deep learning (DL). In this work, a novel...
Article
High-resolution (HR) retinal optical coherence tomography (OCT) images are preferred by the ophthalmologists to diagnose retinal diseases. These images can be obtained by dense scanning of the target retinal region during acquisition. However, a dense scanning increases the image acquisition time and introduces motion artefacts, which corrupt diagn...
Article
The automated analysis of optical coherence tomography (OCT) images can play a crucial role in the diagnosis and management of retinal diseases. The wide variations of the retinal disease manifestations in terms of shape, size, texture and spatial location pose a huge challenge in designing reliable and efficient automated methods. Existing methods...
Article
In this work, vocal tract characteristic changes under the out-of-breath condition are explored. Speaking under the influence of physical exercise is called out-of-breath speech. The change in breathing pattern results in perceptual changes in the produced sound. For vocal tract, the first four formants show a lowering in their average frequency. T...
Article
This work proposes deep learning (DL) based epileptic seizure detection by generating 2D recurrence plot (RP) images of EEG signals for specific brain rhythms. The DL bypasses hand-crafted feature engineering, but extracts feature automatically from input images has displayed significant performance in various domain classification tasks. However,...
Preprint
The electrocardiogram (ECG) based biometric sys- tem has recently gained popularity. Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system. This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services. In this paper,...
Preprint
The electrocardiogram (ECG) based biometric sys- tem has recently gained popularity. Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system. This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services. In this paper,...
Article
In this Letter, the authors propose a variational mode decomposition method for quantifying diagnostic information of myocardial infarction (MI) from the electrocardiogram (ECG) signal. The multiscale mode energy and principal component (PC) of multiscale covariance matrices are used as features. The mode energies determine the strength of the mode...
Article
Electrocardiogram (ECG)-based biometrics are gaining popularity because of its robustness against falsification. In this letter, we have designed a new long short-term memory (LSTM) based framework for person identification using ECG signal. Our model learns the underlying temporal representation of an ECG signal by considering both intra-beat and...
Article
Optical coherence tomography (OCT) enables 3D cross-sectional imaging of the retinal tissues and has become an essential tool for the diagnosis of eye diseases. Clinically, the ophthalmologists examine each cross-sectional image (B- scan) of the 3D OCT volume to diagnose the retinal pathologies. However, this process is time-consuming and tedious....
Article
Posterior myocardial infarction (PMI), also known as “the dark side of the moon", is a lethal heart condition that can cause heart attack if it left untreated. The popularly used standard 12-lead electrocardiogram (12-lead ECG) signals show poor sensitivity for the detection of PMI as it does not have posterior monitoring electrodes. The 3-lead vec...
Article
Age-related macular degeneration (AMD) is the leading cause of progressive vision loss in the elderly. Optical coherence tomography (OCT) is a promising diagnostic tool for early detection and management of AMD. However, the speckle noise and low resolution (LR) of the OCT images affect its diagnostic viabilities. Therefore, denoising and super-res...
Article
Myocardial infarction (MI) is a lethal heart condition that occurs due to the lack of blood flow to the heart tissues. Based on the time from symptoms onset, it is categorized into three severity stages: early MI (EMI), acute MI (AMI), and chronic MI (CMI). Electrocardiogram (ECG) signals are often used to diagnose MI with pathological changes in i...
Article
Deep learning algorithms can offer a reliable automated interpretation of retinal optical coherence tomography (OCT) images to assist clinicians in disease diagnosis and management. However, retinal image processing presents pertinent obstacles such as the struggle of large-scale data acquisition and high cost of annotation. To address this, we hav...
Article
The diagnosis of myocardial infarction (MI) in the presence of other cardiac diseases having similar electrocardiogram (ECG) characteristics that of MI is a challenging problem. Existing automated methods have used the standard 12-lead ECG to detect MI from healthy controls (HC). However, these methods may not provide reliable MI diagnosis in the p...
Chapter
This work focuses on analysing the changes in speech source signals under physical exercise induced out-of-breath conditions. A new database is recorded for sustained vowel phonations (SVPs). Electroglottogram (EGG) and speech signals are recorded in two simultaneous channels. Morphological changes in EGG signal are analysed using a set of five tem...
Chapter
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 vow...
Article
Identification of the macular pathologies at an early stage can prevent vision loss. Similarity in the pathological manifestations of common macular disorders like age related macular degeneration (AMD) and diabetic macular edema (DME) can make manual screening fallible. There is a growing interest among researchers for reliable automated detection...
Article
Transforming a low-resolution electrocardiogram (ECG) recorded using a few electrodes to a high-resolution version can improve the continuous cardiac monitoring environment. Such a transformation can enhance the information available in few leads and increase the spatial resolution. Proper selection of this transformation can provide information re...
Article
Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR...
Article
In this paper, a novel multiscale amplitude feature is proposed using multiresolution analysis (MRA) and the significance of the vocal tract is investigated for emotion classification from the speech signal. MRA decomposes the speech signal into number of sub-band signals. The proposed feature is computed by using sinusoidal model on each sub-band...
Article
Full-text available
This paper presents analysis and classification of a pathological speech called cold speech, which is recorded when the person is suffering from common cold. Nose and throat are affected by the common cold. As nose and throat play an important role in speech production, the speech characteristics are altered during this pathology. In this work, var...
Conference Paper
Full-text available
The present work proposed an approach to characterize the word-final glottal stops in Mizo and Assam Sora language. Generally, glottal stops have more strong glottal and ventricular constriction at the coda position than at the onset. However, the primary source characteristics of glottal stops are irregular glottal cycles, abrupt glottal closing,...
Article
Full-text available
In this work, a novel region switching based classification method is proposed for speech emotion classification using vowel-like regions (VLRs) and non-vowel-like regions (non-VLRs). In literature, normally the entire active speech region is processed for emotion classification. Few studies have been performed on segmented sound units, such as, sy...
Article
In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advant...
Article
Full-text available
In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applica...
Article
Full-text available
The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluatio...
Article
Full-text available
This study explores a novel subspace projection-based approach for analysis of stressed speech. Studies have shown that stress influences the speech production system and it results in a large acoustic variation between the neutral and the stressed speech. This degrades the discrimination capability of an automatic speech recognition system trained...
Conference Paper
In this paper, a novel subspace projection-based approach is explored to analyze the stressed speech. Under stress, the phonetic and the speaker specific attributes exhibit a higher variance in comparison to the neutral speech. This degrades the discrimination capability of an automatic speech recognition (ASR) system trained on neutral speech when...
Conference Paper
This work explores a novel stressed speech recognition method based on sparse representation (SR) of vocal-tract system information, which is extracted using the linear prediction coefficients (LPCs). To accomplish this, the LPCs for neutral and stressed speech are linearly transformed with sparsity onto the common subspace, which consists of param...
Article
Full-text available
In this letter, we propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE...
Conference Paper
Full-text available
The objective of this work is to characterize the intervocalic glottal stops in Assam Sora. Assam Sora is a low resource language of the South Munda language family. Glottal stops are produced with gestures in the deep laryngeal level; hence, the estimated excitation source signal is used in this study to characterize the source dynamics during the...
Article
Mel-frequency cepstral coefficients introduced biologically-inspired features into speech technology, becoming the most commonly used representation for speech, speaker and emotion recognition, and even for applications in music. While this representation is quite popular, it is ambitious to assume that it would provide the best results for every a...
Article
Prolonged diabetes causes severe damage to the vision through leakage of blood and blood constituents over the retina. The effect of the leakage becomes more threatening when these abnormalities involve the macula. This condition is known as diabetic maculopathy and it leads to blindness, if not treated in time. Early detection and proper diagnosis...
Conference Paper
This work analyzes excitation source to characterize glottal stops using integrated linear prediction (ILP) residual, derived by pitch-synchronous (PS) approach. The glottal stop consonant is produced due to laryngeal gesture in the form of constricted glottis. This pressed glottal configuration leads to period to period irregularities, aperiodicit...
Conference Paper
Full-text available
In this work, we have presented a novel method for detection of retinal image features, the optic disc and the fovea, from colour fundus photographs of dilated eyes for Computer-aided Diagnosis(CAD) system. A saliency map based method was used to detect the optic disc followed by an unsupervised probabilistic Latent Semantic Analysis for detection...
Article
Full-text available
In this work, we have presented a novel method for detection of retinal image features, the optic disc and the fovea, from colour fundus photographs of dilated eyes for Computer-aided Diagnosis(CAD) system. A saliency map based method was used to detect the optic disc followed by an unsupervised probabilistic Latent Semantic Analysis for detection...
Conference Paper
In this paper, a novel sparse representation over learned and exemplar dictionaries is explored to estimate the speech information of stressed speech. Stressed speech contains speech and stress informations. The acoustic variabilities are induced due to presence of stress information, which results in degradation of the performance of speech recogn...
Article
Full-text available
In this Letter, a robust third-order tensor decomposition of multi-lead electrocardiogram (MECG) comprising of 12-leads is proposed to reduce the dimension of the storage data. An order-3 tensor structure is employed to represent the MECG data by rearranging the MECG information in three dimensions. The three-dimensions of the formed tensor represe...
Chapter
Synthesis of standard 12-lead electrocardiogram from reduced lead set without losing significant diagnostic information is a major challenge. In this work, we propose a patient specific method for synthesizing 12-lead electrocardiogram from reduced lead set by applying linear regression over the DCT domain. The proposed method is evaluated by stand...
Chapter
In this paper, similarity measurement on different inner product space approach is proposed for analysis of stressed speech. The similarity is measured between neutral speech subspace and stressed speech subspace. Cosine between neutral speech and stressed speech is taken as similarity measurement parameter. It is asssumed that, speech and stress c...
Article
Full-text available
In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction. The method is based on Gaussian scale space based interest map and mathematical morphology. It makes use of support vector machine for classification and location i...
Article
Spatial resolution of ECG can be increased using the information available from a subset of standard 12-lead ECG. This is usually achieved by learning a model between the standard 12-lead and its reduced lead subset. Since ECG signal contains significant amount of diagnostic information, it is important to learn a model which preserves this informa...
Article
This work explores the effect of breathiness component on speech under stress. The breathiness component in a speech signal can be estimated using different features such as period perturbation quotient (PPQ), amplitude perturbation quotient (APQ), harmonic to noise ratio (HNR), glottal to noise excitation ratio (GNER), harmonic energy (HE), harmon...
Article
In this work, a novel approach of linear transformation on speech subspace is used to preserve the properties of speech signal under stress condition. It is assumed that, there is another subspace called as speech subspace which exist and contains the properties of speech signal under neutral and stress conditions. Therefore, speech component of st...
Conference Paper
In this paper, a new compression method based on Higher-Order Singular Value Decomposition (HOSVD) for multilead electrocardiogram (MECG) data is proposed. This method exploits intra-beat and inter-beat correlation present due to quasi-periodic nature of ECG, and inter-lead correlation present among different leads of MECG data as well. The MECG da...
Conference Paper
Electrical activity of the heart recorded by a 12-lead Electrocardiogram is important in detecting cardiac abnormalities. The 12-leads can be seen as the components of heart vector which originates from the center of the heart. These components spread over the frontal and transverse plane and have information about various parts of the heart. Incre...
Article
Independent component analysis is a technique used for separation of statistically independent sources. It can estimate unknown sources from a mixture of sources without any prior knowledge about them. The sources should be non-Gaussian and independent with each other. In this work, multiscale ICA is proposed for medical images (fundus images, MRI...