Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges

Abstract

Dyslexia is a neurological disorder that is characterized by imprecise comprehension of words and generally poor reading performance. It affects a significant population of school-age children, with more occurrences in males, thus, putting them at risk of poor academic performance and low selfesteem for a lifetime. The long-term hope is to have a dyslexia diagnostic method that is informed by neural-biomarkers. In this regard, large numbers of machine learning methods and, more recently, deep learning methods have been implemented across various types of dataset with the above-chance classification accuracy. However, attainment of clinical acceptability of these state-of-the-art methods is bedeviled by certain challenges including lack of biologically-interpretable biomarkers, privacy of dataset and classifiers, hyper-parameter selection/optimization, and overfitting problem among others. This review paper critically analyzes recent machine learning methods for detecting dyslexia and its biomarkers and discusses challenges that require proper attentions from the users of deep learning methods in order to enable them to attain clinically relevance and acceptable level. The review is conducted within the premise of implementation and experimental outcomes for each of the 22 selected articles using the Preferred Reporting Items for Systematic review and Meta-Analyses (PRISMA) protocol, with a view to outlining some critical challenges for achieving high accuracy and reliability of the state-of-the-art machine learning methods. As an evidence-based protocol for reporting in systematic reviews and meta-analyses, PRISMA helps to ensure clarity and transparency of this paper by showing a four-phase flow diagram of the selection process for articles used in this review. It is therefore envisaged that higher classification performance of clinical relevance can be achieved using deep learning models for dyslexia and its biomarkers by addressing identified potential challenges.

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9, 11, 16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”

“…The main tool used to pick up signals was the digital stethoscope, mentioning in 44 articles (IDs = 3, 4, 5, 6, 7, 8, 10,11,13,15,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,35,36,37,38,39,42,44,45,46,48,49,50,51,52,53,54,56). The connectivity of digital stethoscopes occurs, in most equipment, by Bluetooth, not over the Internet, and for this reason these equipments are not considered IoT devices.…”

“…Among the 58 articles selected, 34 works (58.62%,IDs = 1, 2, 5, 6, 10, 19,20,21,22,25,31,32,33,34,35,36,37,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,58) mention care using Machine Learning. Leng et al [30] (36) showed the possible interactions between the electronic stethoscope, the sensor-captured signal decomposition algorithm, machine learning techniques and cardiac sound segmentation.…”

“…AI-based cardiac auscultation in the context of Machine Learning (ML) [37] uses preprocessing algorithms for signal acquisition, so that training is later developed to detect abnormal heart rate patterns by applying models, for example, of convolutional neural networks (CNN) [38].…”

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…AI-based cardiac auscultation in the context of Machine Learning (ML) [35] uses preprocessing algorithms for signal acquisition, so that training is later developed to detect abnormal heart rate patterns by applying models, for example, of convolutional neural networks (CNN) [36].…”