Applied Sciences

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16 pages, 2703 KiB

Open AccessArticle

Automatic Robust Crackle Detection and Localization Approach Using AR-Based Spectral Estimation and Support Vector Machine

by Loredana Daria Mang, Julio José Carabias-Orti, Francisco Jesús Canadas-Quesada, Juan de la Torre-Cruz, Antonio Muñoz-Montoro, Pablo Revuelta-Sanz and Eilas Fernandez Combarro

Appl. Sci. 2023, 13(19), 10683; https://doi.org/10.3390/app131910683 - 26 Sep 2023

Cited by 2 | Viewed by 1169

Abstract

Auscultation primarily relies upon the acoustic expertise of individual doctors in identifying, through the use of a stethoscope, the presence of abnormal sounds such as crackles because the recognition of these sound patterns has critical importance in the context of early detection and [...] Read more.

Auscultation primarily relies upon the acoustic expertise of individual doctors in identifying, through the use of a stethoscope, the presence of abnormal sounds such as crackles because the recognition of these sound patterns has critical importance in the context of early detection and diagnosis of respiratory pathologies. In this paper, we propose a novel method combining autoregressive (AR)-based spectral features and a support vector machine (SVM) classifier to detect the presence of crackle events and their temporal location within the input signal. A preprocessing stage is performed to discard information out of the band of interest and define the segments for short-time signal analysis. The AR parameters are estimated for each segment to be classified by means of support vector machine (SVM) classifier into crackles and normal lung sounds using a set of synthetic crackle waveforms that have been modeled to train the classifier. A dataset composed of simulated and real coarse and fine crackles sound signals was created with several signal-to-noise (SNR) ratios to evaluate the robustness of the proposed method. Each simulated and real signal was mixed with noise that shows the same spectral energy distribution as typically found in breath noise from a healthy subject. This study makes a significant contribution by achieving competitive results. The proposed method yields values ranging from 80% in the lowest signal-to-noise ratio scenario to a perfect 100% in the highest signal-to-noise ratio scenario. Notably, these results surpass those of other methods presented by a margin of at least 15%. The combination of an autoregressive (AR) model with a support vector machine (SVM) classifier offers an effective solution for detecting the presented events. This approach exhibits enhanced robustness against variations in the signal-to-noise ratio that the input signals may encounter. Full article

(This article belongs to the Special Issue Pattern Recognition and Artificial Intelligence in Biomedical Signal Processing)

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18 pages, 4459 KiB

Open AccessArticle

The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion Classification

by Zubaidah Al-Mashhadani, Nasrin Bayat, Ibrahim F. Kadhim, Renoa Choudhury and Joon-Hyuk Park

Appl. Sci. 2023, 13(14), 8274; https://doi.org/10.3390/app13148274 - 17 Jul 2023

Viewed by 1935

Abstract

Electroencephalography (EEG) signals have diverse applications in brain-computer interfaces (BCIs), neurological condition diagnoses, and emotion recognition across healthcare, education, and entertainment domains. This paper presents a robust method that leverages Riemannian geometry to enhance the accuracy of EEG-based emotion classification. The proposed approach [...] Read more.

Electroencephalography (EEG) signals have diverse applications in brain-computer interfaces (BCIs), neurological condition diagnoses, and emotion recognition across healthcare, education, and entertainment domains. This paper presents a robust method that leverages Riemannian geometry to enhance the accuracy of EEG-based emotion classification. The proposed approach involves adaptive feature extraction using principal component analysis (PCA) in the Euclidean space to capture relevant signal characteristics and improve classification performance. Covariance matrices are derived from the extracted features and projected onto the Riemannian manifold. Emotion classification is performed using the minimum distance to Riemannian mean (MDRM) classifier. The effectiveness of the method was evaluated through experiments on four datasets, DEAP, DREAMER, MAHNOB, and SEED, demonstrating its generalizability and consistent accuracy improvement across different scenarios. The classification accuracy and robustness were compared with several state-of-the-art classification methods, which supports the validity and efficacy of using Riemannian geometry for enhancing the accuracy of EEG-based emotion classification. Full article

(This article belongs to the Special Issue Pattern Recognition and Artificial Intelligence in Biomedical Signal Processing)

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21 pages, 803 KiB

Open AccessArticle

A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

by Ioannis G. Tsoulos and Alexandros Tzallas

Appl. Sci. 2023, 13(14), 8124; https://doi.org/10.3390/app13148124 - 12 Jul 2023

Cited by 1 | Viewed by 1113

Abstract

The problem of data classification or data fitting is widely applicable in a multitude of scientific areas, and for this reason, a number of machine learning models have been developed. However, in many cases, these models present problems of overfitting and cannot generalize [...] Read more.

The problem of data classification or data fitting is widely applicable in a multitude of scientific areas, and for this reason, a number of machine learning models have been developed. However, in many cases, these models present problems of overfitting and cannot generalize satisfactorily to unknown data. Furthermore, in many cases, many of the features of the input data do not contribute to learning, or there may even be hidden correlations between the features of the dataset. The purpose of the proposed method is to significantly reduce data classification or regression errors through the usage of a technique that utilizes the particle swarm optimization method and grammatical evolution. This method is divided into two phases. In the first phase, artificial features are constructed using grammatical evolution, and the progress of the creation of these features is controlled by the particle swarm optimization method. In addition, this new technique utilizes penalty factors to limit the generated features to a range of values to make training machine learning models more efficient. In the second phase of the proposed technique, these features are exploited to transform the original dataset, and then any machine learning method can be applied to this dataset. The performance of the proposed method was measured on some benchmark datasets from the relevant literature. Also, the method was tested against a series of widely used machine learning models. The experiments performed showed a significant improvement of 30% on average in the classification datasets and an even greater improvement of 60% in the data fitting datasets. Full article

(This article belongs to the Special Issue Pattern Recognition and Artificial Intelligence in Biomedical Signal Processing)

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17 pages, 7152 KiB

Open AccessArticle

Detection of Human Visceral Leishmaniasis Parasites in Microscopy Images from Bone Marrow Parasitological Examination

by Clésio Gonçalves, Armando Borges, Viviane Dias, Júlio Marques, Bruno Aguiar, Carlos Costa and Romuere Silva

Appl. Sci. 2023, 13(14), 8076; https://doi.org/10.3390/app13148076 - 11 Jul 2023

Cited by 6 | Viewed by 1756

Abstract

Visceral Leishmaniasis (VL) is a neglected disease that affects between 50,000 and 90,000 new cases annually worldwide. In Brazil, VL causes about 3500 cases/per year. This chronic disease can lead to death in 90% of untreated cases. Thus, it is necessary to study [...] Read more.

Visceral Leishmaniasis (VL) is a neglected disease that affects between 50,000 and 90,000 new cases annually worldwide. In Brazil, VL causes about 3500 cases/per year. This chronic disease can lead to death in 90% of untreated cases. Thus, it is necessary to study safe technologies for diagnosing, treating, and controlling VL. Specialized laboratories carry out the VL diagnosis, and this step has a significant automation power through methods based on computational tools. The gold standard for detecting VL is the microscopy of material aspirated from the bone marrow to search for amastigotes. This work aims to assist in detecting amastigotes from microscopy images using deep learning techniques. The proposed methodology consists of segmenting the Leishmania parasites in the images, precisely indicating the location of the amastigotes in the image. In the detection of VL parasites, in this methodology, a Dice of 80.4% was obtained, Intersection over Union (IoU) of 75.2%, Accuracy of 99.1%, Precision of 81.5%, Sensitivity of 72.2%, Specificity of 99.6%, and Area under the Receiver Operating Characteristics Curve (AUC) of 86.5%. The results are promising and demonstrate that deep learning models trained with images of microscopy slides of biological material can precisely help the specialist detect VL in humans. Full article

(This article belongs to the Special Issue Pattern Recognition and Artificial Intelligence in Biomedical Signal Processing)

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13 pages, 2184 KiB

Open AccessArticle

Dealing with Unreliable Annotations: A Noise-Robust Network for Semantic Segmentation through A Transformer-Improved Encoder and Convolution Decoder

by Ziyang Wang and Irina Voiculescu

Appl. Sci. 2023, 13(13), 7966; https://doi.org/10.3390/app13137966 - 7 Jul 2023

Cited by 3 | Viewed by 1618

Abstract

Conventional deep learning methods have shown promising results in the medical domain when trained on accurate ground truth data. Pragmatically, due to constraints like lack of time or annotator inexperience, the ground truth data obtained from clinical environments may not always be impeccably [...] Read more.

Conventional deep learning methods have shown promising results in the medical domain when trained on accurate ground truth data. Pragmatically, due to constraints like lack of time or annotator inexperience, the ground truth data obtained from clinical environments may not always be impeccably accurate. In this paper, we investigate whether the presence of noise in ground truth data can be mitigated. We propose an innovative and efficient approach that addresses the challenge posed by noise in segmentation labels. Our method consists of four key components within a deep learning framework. First, we introduce a Vision Transformer-based modified encoder combined with a convolution-based decoder for the segmentation network, capitalizing on the recent success of self-attention mechanisms. Second, we consider a public CT spine segmentation dataset and devise a preprocessing step to generate (and even exaggerate) noisy labels, simulating real-world clinical situations. Third, to counteract the influence of noisy labels, we incorporate an adaptive denoising learning strategy (ADL) into the network training. Finally, we demonstrate through experimental results that the proposed method achieves noise-robust performance, outperforming existing baseline segmentation methods across multiple evaluation metrics. Full article

(This article belongs to the Special Issue Pattern Recognition and Artificial Intelligence in Biomedical Signal Processing)

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16 pages, 4031 KiB

Open AccessArticle

Effect of Subject-Specific Region of Interest on Motor Imagery Brain–Computer Interface

by Eltaf Abdalsalam Mohamed, Ibrahim Khalil Adam and Mohd Zuki Yusoff

Appl. Sci. 2023, 13(11), 6364; https://doi.org/10.3390/app13116364 - 23 May 2023

Cited by 1 | Viewed by 1184

Abstract

A brain–computer interface (BCI), as a solution to disabled people’s concerns, has drawn attention in biomedical engineering over the last decade. However, the most existing brain–computer interface systems are based on the time or frequency domain of feature extraction, and it is associated [...] Read more.

A brain–computer interface (BCI), as a solution to disabled people’s concerns, has drawn attention in biomedical engineering over the last decade. However, the most existing brain–computer interface systems are based on the time or frequency domain of feature extraction, and it is associated with inaccurate detection of event-related desynchronization (ERD). In this study, a new algorithm relating to subject-specific regions of interest (ROIs) with intrinsic time-scale decomposition (ITD) was investigated to achieve satisfactory classification accuracy. ROI-based discrete wavelet transform (DWT) combined with an artificial neural network was used to validate the ROI-based ITD method. Experimentally recorded data of motor imagery movement tasks (right hand, left hand, both hands and both feet) were collected from 15 subjects. The parameters of the subject-specific regions of interest were investigated and optimized. An optimal condition was observed at a specific region of interest and the accuracy increased by 12.76 to 15.17% compared to that without ROI estimation. ITD showed higher classification accuracy, sensitivity, specificity and Kappa coefficient of 9.47%, 8.99%, 9.79% and 12.09%, respectively, for the four classes of motor imagery movements compared to DWT. The developed ITD model was validated using the dataset from BCI Competition IV. On average, ITD with ROIs showed 8.56% and 7.32% higher classification accuracy compared to common spatial patents (CSP) and DWT with ROIs. Full article

(This article belongs to the Special Issue Pattern Recognition and Artificial Intelligence in Biomedical Signal Processing)

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13 pages, 862 KiB

Open AccessArticle

Characterization of the Intelligibility of Vowel–Consonant–Vowel (VCV) Recordings in Five Languages for Application in Speech-in-Noise Screening in Multilingual Settings

by Giulia Rocco, Giuliano Bernardi, Randall Ali, Toon van Waterschoot, Edoardo Maria Polo, Riccardo Barbieri and Alessia Paglialonga

Appl. Sci. 2023, 13(9), 5344; https://doi.org/10.3390/app13095344 - 25 Apr 2023

Viewed by 1372

Abstract

The purpose of this study is to characterize the intelligibility of a corpus of Vowel–Consonant–Vowel (VCV) stimuli recorded in five languages (English, French, German, Italian and Portuguese) in order to identify a subset of stimuli for screening individuals of unknown language during speech-in-noise [...] Read more.

The purpose of this study is to characterize the intelligibility of a corpus of Vowel–Consonant–Vowel (VCV) stimuli recorded in five languages (English, French, German, Italian and Portuguese) in order to identify a subset of stimuli for screening individuals of unknown language during speech-in-noise tests. The intelligibility of VCV stimuli was estimated by combining the psychometric functions derived from the Short-Time Objective Intelligibility (STOI) measure with those derived from listening tests. To compensate for the potential increase in speech recognition effort in non-native listeners, stimuli were selected based on three criteria: (i) higher intelligibility; (ii) lower variability of intelligibility; and (iii) shallower psychometric function. The observed intelligibility estimates show that the three criteria for application in multilingual settings were fulfilled by the set of VCVs in English (average intelligibility from 1% to 8% higher; SRT from 4.01 to 2.04 dB SNR lower; average variability up to four times lower; slope from 0.35 to 0.68%/dB SNR lower). Further research is needed to characterize the intelligibility of these stimuli in a large sample of non-native listeners with varying degrees of hearing loss and to determine the possible effects of hearing loss and native language on VCV recognition. Full article

(This article belongs to the Special Issue Pattern Recognition and Artificial Intelligence in Biomedical Signal Processing)

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18 pages, 951 KiB

Open AccessArticle

Fuzzy Windows with Gaussian Processed Labels for Ordinal Image Scoring Tasks

by Cheng Kang, Xujing Yao and Daniel Novak

Appl. Sci. 2023, 13(6), 4019; https://doi.org/10.3390/app13064019 - 22 Mar 2023

Cited by 2 | Viewed by 1563

Abstract

In this paper, we propose a Fuzzy Window with the Gaussian Processed Label (FW-GPL) method to mitigate the overlap problem in the neighboring ordinal category when scoring images. Many published conventional methods treat this challenge as a traditional regression problem and make a [...] Read more.

In this paper, we propose a Fuzzy Window with the Gaussian Processed Label (FW-GPL) method to mitigate the overlap problem in the neighboring ordinal category when scoring images. Many published conventional methods treat this challenge as a traditional regression problem and make a strong assumption that each ordinal category owns an adequate intrinsic rank to outline its distribution. Our FW-GPL method aims to refine the ordinal label pattern by using two novel techniques: (1) assembling fuzzy logic to the fully connected layer of convolution neural networks and (2) transforming the ordinal labels with a Gaussian process. Specifically, it incorporates a heuristic fuzzy logic from the ordinal characteristic and simultaneously plugs in ordinal distribution shapes that penalize the difference between the targeted label and its neighbors to ensure a concentrated regional distribution. Accordingly, the function of these proposed windows is leveraged to minimize the influence of majority classes that mislead the prediction of minority samples. Our model is specifically designed to carefully avoid partially missing continuous facial-age segments. It can perform competitively when using the whole continuous facial-age dataset. Extensive experimental results on three facial-aging datasets and one ambiguous medical dataset demonstrate that our FW-GPL can achieve compelling performance results compared to the State-Of-The-Art (SOTA). Full article

(This article belongs to the Special Issue Pattern Recognition and Artificial Intelligence in Biomedical Signal Processing)

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