1
|
Zhang X, Landsness EC, Miao H, Chen W, Tang MJ, Brier LM, Culver JP, Lee JM, Anastasio MA. Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data. J Neurosci Methods 2024; 411:110250. [PMID: 39151658 DOI: 10.1016/j.jneumeth.2024.110250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 08/03/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
BACKGROUND Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. NEW METHOD A hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. RESULTS Sleep states were classified with an accuracy of 84 % and Cohen's κ of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. COMPARISON WITH EXISTING METHOD On a held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ of 0.65 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep research.
Collapse
Affiliation(s)
- Xiaohui Zhang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Eric C Landsness
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Hanyang Miao
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Wei Chen
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Michelle J Tang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lindsey M Brier
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Joseph P Culver
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO 63130, USA; Department of Electrical and Systems Engineering, Washington University School of Engineering, St. Louis, MO 63130, USA; Department of Physics, Washington University School of Arts and Science, St. Louis, Mo 63130, USA
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO 63130, USA
| | - Mark A Anastasio
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
| |
Collapse
|
2
|
Yang Z, Jin A, Li Y, Yu X, Xu X, Wang J, Li Q, Guo X, Liu Y. A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection. Sci Rep 2024; 14:20828. [PMID: 39242748 PMCID: PMC11379913 DOI: 10.1038/s41598-024-71700-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 08/30/2024] [Indexed: 09/09/2024] Open
Abstract
The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essential in the fields of biomedical engineering and clinical cardiac disease diagnosis. Intelligent ECG diagnosis techniques encompass Recurrent Neural Networks (RNN), Transformers, and Convolutional Neural Networks (CNN). While CNN is capable of extracting local spatial information from images, it lacks the ability to learn global spatial features and temporal memory features. Conversely, RNN relies on time and can retain significant sequential features. However, they are not proficient in extracting lengthy dependencies of sequence data in practical scenarios. The self-attention mechanism in the Transformer model has the capability of global feature extraction, but it does not adequately prioritize local features and cannot extract spatial and channel features. This paper proposes STFAC-ECGNet, a model that incorporates CAMV-RNN block, CBMV-CNN block, and TSEF block to enhance the performance of the model by integrating the strengths of CNN, RNN, and Transformer. The CAMV-RNN block incorporates a coordinated adaptive simplified self-attention module that adaptively carries out global sequence feature retention and enhances spatial-temporal information. The CBMV-CNN block integrates spatial and channel attentional mechanism modules in a skip connection, enabling the fusion of spatial and channel information. The TSEF block implements enhanced multi-scale fusion of image spatial and sequence temporal features. In this study, comprehensive experiments were conducted using the PTB-XL large publicly available ECG dataset and the China Physiological Signal Challenge 2018 (CPSC2018) database. The results indicate that STFAC-ECGNet surpasses other cutting-edge techniques in multiple tasks, showcasing robustness and generalization.
Collapse
Affiliation(s)
- Zicong Yang
- School of Computer Science, Zhuhai College of Science and Technology, Zhuhai, 519041, China
| | - Aitong Jin
- School of Big Data, Zhuhai College of Science and Technology, Zhuhai, 519041, China
| | - Yu Li
- School of Big Data, Zhuhai College of Science and Technology, Zhuhai, 519041, China.
| | - Xuyi Yu
- Intelligent Optics and Photonics Research Center, Jiaxing Research Institute Zhejiang University, Jiaxing, 314011, China
| | - Xi Xu
- School of Business, Zhejiang Wanli University, Ningbo, 315100, China
| | - Junxi Wang
- School of Mechanical Engineering, Zhuhai College of Science and Technology, Zhuhai, 519041, China
| | - Qiaolin Li
- School of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Xiaoyan Guo
- School of Computer Science, Zhuhai College of Science and Technology, Zhuhai, 519041, China.
| | - Yan Liu
- School of Computer Science, Zhuhai College of Science and Technology, Zhuhai, 519041, China
| |
Collapse
|
3
|
Obuchi SP, Kojima M, Suzuki H, Garbalosa JC, Imamura K, Ihara K, Hirano H, Sasai H, Fujiwara Y, Kawai H. Artificial intelligence detection of cognitive impairment in older adults during walking. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e70012. [PMID: 39328904 PMCID: PMC11424983 DOI: 10.1002/dad2.70012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 08/19/2024] [Accepted: 08/25/2024] [Indexed: 09/28/2024]
Abstract
INTRODUCTION To detect early cognitive impairment in community-dwelling older adults, this study explored the viability of artificial intelligence (AI)-assisted linear acceleration and angular velocity analysis during walking. METHODS This cross-sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini-Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides. RESULTS The models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets. DISCUSSION AI-enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention. Highlights Artificial intelligence (AI)-enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community-dwelling cohort.AI-assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia.
Collapse
Affiliation(s)
- Shuichi P. Obuchi
- Research Team for Human CareTokyo Metropolitan Institute for Geriatrics and GerontologyItabashi‐kuTokyoJapan
| | - Motonaga Kojima
- Department of Physical TherapyUniversity of Tokyo Health SciencesTama‐shiTokyoJapan
| | - Hiroyuki Suzuki
- Research Team for Social Participation and Community HealthTokyo Metropolitan Institute for Geriatrics and GerontologyItabashi‐kuTokyoJapan
| | - Juan C. Garbalosa
- Department of Physical TherapyQuinnipiac UniversityHamdenConnecticutUSA
| | - Keigo Imamura
- Research Team for Human CareTokyo Metropolitan Institute for Geriatrics and GerontologyItabashi‐kuTokyoJapan
| | - Kazushige Ihara
- Graduate School of MedicineHirosaki UniversityHirsaki‐shiAomoriJapan
| | - Hirohiko Hirano
- Research Team for Promoting Independence and Mental HealthTokyo Metropolitan Institute for Geriatrics and GerontologyItabashi‐kuTokyoJapan
| | - Hiroyuki Sasai
- Research Team for Promoting Independence and Mental HealthTokyo Metropolitan Institute of Geriatrics and GerontologyItabashi‐kuTokyoJapan
| | - Yoshinori Fujiwara
- Tokyo Metropolitan Institute for Geriatrics and GerontologyItabashi‐kuTokyoJapan
| | - Hisashi Kawai
- Research Team for Human CareTokyo Metropolitan Institute for Geriatrics and GerontologyItabashi‐kuTokyoJapan
| |
Collapse
|
4
|
Zhang Y, Qu H, Tian Y, Na F, Yan J, Wu Y, Cui X, Li Z, Zhao M. PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images. BMC Cancer 2023; 23:936. [PMID: 37789252 PMCID: PMC10548640 DOI: 10.1186/s12885-023-11364-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 09/04/2023] [Indexed: 10/05/2023] Open
Abstract
OBJECTIVE To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. METHODS We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. RESULTS Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. CONCLUSIONS In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.
Collapse
Affiliation(s)
- Yuchong Zhang
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China
| | - Hui Qu
- College of Medicine and Biological Information Engineering, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang, 110819, Liaoning Province, China
| | - Yumeng Tian
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China
| | - Fangjian Na
- Network Information Center, China Medical University, NO.77 Puhe Road, Shenbei New District, Shenyang, Liaoning Province, 110122, China
| | - Jinshan Yan
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China
| | - Ying Wu
- Phase I Clinical Trails Center, the First Hospital of China Medical University, 210 1st Baita Street, Hunnan Distriction, Shenyang, Liaoning Province, 110101, China
| | - Xiaoyu Cui
- College of Medicine and Biological Information Engineering, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang, 110819, Liaoning Province, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China.
| | - Zhi Li
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China.
| | - Mingfang Zhao
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China.
| |
Collapse
|
5
|
Budaraju D, Neelapu BC, Pal K, Jayaraman S. Stacked machine learning models to classify atrial disorders based on clinical ECG features: a method to predict early atrial fibrillation. BIOMED ENG-BIOMED TE 2023:bmt-2022-0430. [PMID: 36963433 DOI: 10.1515/bmt-2022-0430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 02/20/2023] [Indexed: 03/26/2023]
Abstract
OBJECTIVES Atrial Tachycardia (AT) and Left Atrial Enlargement (LAE) are atrial diseases that are significant precursors to Atrial Fibrillation (AF). There are ML models for ECG classification; clinical features-based classification is required. The suggested work aims to create stacked ML models that categorize Sinus Rhythm (SR), Sinus Tachycardia (ST), AT, and LAE signals based on clinical parameters for AF prognosis. METHODS The classification was based on thirteen clinical parameters, such as amplitude, time domain ECG aspects, and P-Wave Indices (PWI), such as the ratio of P-wave length and amplitude ((P (ms)/P (µV)), P-wave area (µV*ms), and P-wave terminal force (PTFV1(µV*ms). Apart from classifying the ECG signals, the stacked ML models prioritized the clinical features using a pie formula-based technique. RESULTS The Stack 1 model achieves 99% accuracy, sensitivity, precision, and F1 score, while the Stack 2 model achieves 91%, 91%, 94%, and 92% for identifying SR, ST, LAE, and AT, respectively. Both stack models obtained a computational time of 0.06 seconds. PTFV1 (µV*ms), P (ms)/P (µV)), and P-wave area (µV*ms) were ranked as crucial clinical features. CONCLUSION Clinical feature-based stacking ML models may help doctors obtain insight into important clinical ECG aspects for early AF prediction.
Collapse
Affiliation(s)
- Dhananjay Budaraju
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, India
| | - Bala Chakravarthy Neelapu
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, India
| | - Kunal Pal
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, India
| | - Sivaraman Jayaraman
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, India
| |
Collapse
|
6
|
Pham TD, Ravi V, Fan C, Luo B, Sun XF. Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 11:87-95. [PMID: 36704244 PMCID: PMC9870269 DOI: 10.1109/jtehm.2022.3229561] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/06/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Over a decade, tissues dissected adjacent to primary tumors have been considered "normal" or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict the survival rate of cancer patients using NATs can open a new door to selecting optimal treatments for cancer and discovering biomarkers. METHODS This paper introduces an artificial intelligence (AI) approach that uses NATs for predicting the 5-year survival of pre-operative radiotherapy patients with rectal cancer. The new approach combines pre-trained deep learning, nonlinear dynamics, and long short-term memory to classify immunohistochemical images of RhoB protein expression on NATs. RESULTS Ten-fold cross-validation results show 88% accuracy of prediction obtained from the new approach, which is also higher than those provided from baseline methods. CONCLUSION Preliminary results not only add objective evidence to recent findings of NATs' molecular characteristics using state-of-the-art AI methods, but also contribute to the discovery of RhoB expression on NATs in rectal-cancer patients. CLINICAL IMPACT The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.
Collapse
Affiliation(s)
- Tuan D Pham
- Center for Artificial IntelligencePrince Mohammad Bin Fahd University Khobar 31952 Saudi Arabia
| | - Vinayakumar Ravi
- Center for Artificial IntelligencePrince Mohammad Bin Fahd University Khobar 31952 Saudi Arabia
| | - Chuanwen Fan
- Department of OncologyLinkoping University 58185 Linkoping Sweden
- Department of Biomedical and Clinical SciencesLinkoping University 58185 Linkoping Sweden
| | - Bin Luo
- Department of OncologyLinkoping University 58185 Linkoping Sweden
- Department of Biomedical and Clinical SciencesLinkoping University 58185 Linkoping Sweden
- Department of Gastrointestinal SurgerySichuan Provincial People's Hospital Chengdu 610032 China
| | - Xiao-Feng Sun
- Department of OncologyLinkoping University 58185 Linkoping Sweden
- Department of Biomedical and Clinical SciencesLinkoping University 58185 Linkoping Sweden
| |
Collapse
|
7
|
Fatema K, Montaha S, Rony MAH, Azam S, Hasan MZ, Jonkman M. A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images. Biomedicines 2022; 10:2835. [PMID: 36359355 PMCID: PMC9687837 DOI: 10.3390/biomedicines10112835] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/15/2022] [Accepted: 11/03/2022] [Indexed: 12/01/2023] Open
Abstract
Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.
Collapse
Affiliation(s)
- Kaniz Fatema
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh
| | - Sidratul Montaha
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh
| | - Md. Awlad Hossen Rony
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh
| | - Sami Azam
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT 0909, Australia
| | - Md. Zahid Hasan
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh
| | - Mirjam Jonkman
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT 0909, Australia
| |
Collapse
|
8
|
Rethinking the Methods and Algorithms for Inner Speech Decoding and Making Them Reproducible. NEUROSCI 2022. [DOI: 10.3390/neurosci3020017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
This study focuses on the automatic decoding of inner speech using noninvasive methods, such as Electroencephalography (EEG). While inner speech has been a research topic in philosophy and psychology for half a century, recent attempts have been made to decode nonvoiced spoken words by using various brain–computer interfaces. The main shortcomings of existing work are reproducibility and the availability of data and code. In this work, we investigate various methods (using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Networks (LSTM)) for the detection task of five vowels and six words on a publicly available EEG dataset. The main contributions of this work are (1) subject dependent vs. subject-independent approaches, (2) the effect of different preprocessing steps (Independent Component Analysis (ICA), down-sampling and filtering), and (3) word classification (where we achieve state-of-the-art performance on a publicly available dataset). Overall we achieve a performance accuracy of 35.20% and 29.21% when classifying five vowels and six words, respectively, in a publicly available dataset, using our tuned iSpeech-CNN architecture. All of our code and processed data are publicly available to ensure reproducibility. As such, this work contributes to a deeper understanding and reproducibility of experiments in the area of inner speech detection.
Collapse
|