Explainability of CNN-based Alzheimer's disease detection from online handwriting

With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer's disease detection, the widespread adoption of deep learning models raises concerns about their explainability. The lack of explainability in deep learning models for online handwriting analysis is a critical gap in the literature in the context of Alzheimer's disease detection. This paper addresses this challenge by interpreting predictions from a Convolutional Neural Network applied to multivariate time series data, generated by online handwriting data associated with continuous loop series handwritten on a graphical tablet. Our explainability methods reveal distinct motor behavior characteristics for healthy individuals and those diagnosed with Alzheimer's. Healthy subjects exhibited consistent, smooth movements, while Alzheimer's patients demonstrated erratic patterns marked by abrupt stops and direction changes. This emphasizes the critical role of explainability in translating complex models into clinically relevant insights. Our research contributes to the enhancement of early diagnosis, providing significant and reliable insights to stakeholders involved in patient care and intervention strategies. Our work bridges the gap between machine learning predictions and clinical insights, fostering a more effective and understandable application of advanced models for Alzheimer's disease assessment.

Utilizing deep learning models in an intelligent spiral drawing classification system for Parkinson's disease classification

Introduction

Parkinson's disease (PD) is a neurodegenerative illness that impairs normal human movement. The primary cause of PD is the deficiency of dopamine in the human brain. PD also leads to several other challenges, including insomnia, eating disturbances, excessive sleepiness, fluctuations in blood pressure, sexual dysfunction, and other issues.

Methods

The suggested system is an extremely promising technological strategy that may help medical professionals provide accurate and unbiased disease diagnoses. This is accomplished by utilizing significant and unique traits taken from spiral drawings connected to Parkinson's disease. While PD cannot be cured, early administration of drugs may significantly improve the condition of a patient with PD. An expeditious and accurate clinical classification of PD ensures that efficacious therapeutic interventions can commence promptly, potentially impeding the advancement of the disease and enhancing the quality of life for both patients and their caregivers. Transfer learning models have been applied to diagnose PD by analyzing important and distinctive characteristics extracted from hand-drawn spirals. The studies were carried out in conjunction with a comparison analysis employing 102 spiral drawings. This work enhances current research by analyzing the effectiveness of transfer learning models, including VGG19, InceptionV3, ResNet50v2, and DenseNet169, for identifying PD using hand-drawn spirals.

Results

Transfer machine learning models demonstrate highly encouraging outcomes in providing a precise and reliable classification of PD. Actual results demonstrate that the InceptionV3 model achieved a high accuracy of 89% when learning from spiral drawing images and had a superior receiver operating characteristic (ROC) curve value of 95%.

Discussion

The comparison results suggest that PD identification using these models is currently at the forefront of PD research. The dataset will be enlarged, transfer learning strategies will be investigated, and the system's integration into a comprehensive Parkinson's monitoring and evaluation platform will be looked into as future research areas. The results of this study could lead to a better quality of life for Parkinson's sufferers, individualized treatment, and an early classification.

Intelligent Human-Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human-computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios.

Intensive training programme improves handwriting in a community cohort of people with Parkinson's disease

BACKGROUND

People with Parkinson's disease (PwP) often report problems with their handwriting before they receive a formal diagnosis. Many PwP suffer from deteriorating handwriting throughout their illness, which has detrimental effects on many aspects of their quality of life.

AIMS

To assess a 6-week online training programme aimed at improving handwriting of PwP.

METHODS

Handwriting samples from a community-based cohort of PwP (n = 48) were analysed using systematic detection of writing problems (SOS-PD) by two independent raters, before and after a 6-week remotely monitored physiotherapy-led training programme. Inter-rater variability on multiple measures of handwriting quality was analysed. The handwriting data was analysed using pre-/post-design in the same individuals. Multiple aspects of the handwriting samples were assessed, including writing fluency, transitions between letters, regularity in letter size, word spacing, and straightness of lines.

RESULTS

Analysis of inter-rater reliability showed high agreement for total handwriting scores and letter size, as well as speed and legibility scores, whereas there were mixed levels of inter-rater reliability for other handwriting measures. Overall handwriting quality (p = 0.001) and legibility (p = 0.009) significantly improved, while letter size (p = 0.012), fluency (p = 0.001), regularity of letter size (p = 0.009), and straightness of lines (p = 0.036) were also enhanced.

CONCLUSIONS

The results of this study show that this 6-week intensive remotely-monitored physiotherapy-led handwriting programme improved handwriting in PwP. This is the first study of its kind to use this tool remotely, and it demonstrated that the SOS-PD is reliable for measuring handwriting in PwP.

How word semantics and phonology affect handwriting of Alzheimer's patients: A machine learning based analysis

Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer's disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance: the best result was obtained on a non-regular, reaching an accuracy close to 90%.

Handwriting Markers for the Onset of Alzheimer's Disease

INTRODUCTION

Alzheimer's disease has an impact on handwriting (AD). Numerous researchers reported that fact. Therefore, examining handwriting characteristics could be a useful way to screen for AD. The aim of the article is to present the reliability and effectiveness of the AD-HS tool.

METHODS

Most of the existing studies examine either linguistic manifestations of writing or certain motor functions. However, handwriting is a complex of cognitive and motor activities. Since the influence of AD on handwriting is individual, it is important to analyze the complete set of handwriting features. The AD-HS instrument is based on this principle. Validation of the AD-HS instrument for revealing cognitive impairment in AD-diagnosed persons in comparison to the control group. The study is based on the evaluation of free handwritten texts. AD-HS includes 40 handwriting and 2 linguistic features of handwritten texts. It is based on the standard protocol for handwriting analysis. The cumulative evaluation of all features builds a quantitative AD-Indicator (ADI) as a marker of possible AD conditions. The analyzed experiment includes 53 AD-diagnosed persons and a control group of 192 handwriting specimens from the existing database.

RESULTS

AD-HS shows a distinct difference in evaluated ADI for the participants (the mean value equals 0.49) and the control group (the mean value equals 0.28).

CONCLUSION

The handwriting marker of AD could be an effective supplement instrument for earlier screening. It is also useful when traditional biomarkers and neurological tests could not be applied. AD-HS can accompany therapy as an indication of its effect on a person.

A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks

There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings.

Direct mass spectrometric imaging of document handwriting with laser desorption ionization and post ultraviolet photodissociation

Handwriting represents personal education and physical or psychological states. This work describes a chemical imaging technique for document evaluation that combines laser desorption ionization with post ultraviolet photo-induced dissociation (LDI-UVPD) in mass spectrometry. Taken the advantages of chromophores in ink dyes, handwriting papers were subjected to direct laser desorption ionization without additional matrix materials. It is a surface-sensitive analytical method that uses a low intensity pulsed laser at 355 nm to remove chemical components from very outermost surfaces of overlapped handwritings. Meanwhile, the transfer of photoelectrons to those compounds leads to the ionization and the formation of radical anions. The gentle evaporation and ionization property enable the dissection of chronological orders. Paper documents maintain intact without extensive damages after laser irradiation. The evolving plume resulting from the irradiation of the 355 nm laser is fired by the second ultraviolet laser at 266 nm that is in parallel to the sample surface. In contrast to collision activated dissociation in tandem MS/MS, such post ultraviolet photodissociation generates much more different fragment ions through electron-directed specific cleavages of chemical bonds. LDI-UVPD can not only provide graphic representation of chemical components but also reveal hidden dynamic features such as alterations, pressures and aging.

Alzheimer's early detection in post-acute COVID-19 syndrome: a systematic review and expert consensus on preclinical assessments

Introduction

The risk of developing Alzheimer's disease (AD) in older adults increasingly is being discussed in the literature on Post-Acute COVID-19 Syndrome (PACS). Remote digital Assessments for Preclinical AD (RAPAs) are becoming more important in screening for early AD, and should always be available for PACS patients, especially for patients at risk of AD. This systematic review examines the potential for using RAPA to identify impairments in PACS patients, scrutinizes the supporting evidence, and describes the recommendations of experts regarding their use.

Methods

We conducted a thorough search using the PubMed and Embase databases. Systematic reviews (with or without meta-analysis), narrative reviews, and observational studies that assessed patients with PACS on specific RAPAs were included. The RAPAs that were identified looked for impairments in olfactory, eye-tracking, graphical, speech and language, central auditory, or spatial navigation abilities. The recommendations' final grades were determined by evaluating the strength of the evidence and by having a consensus discussion about the results of the Delphi rounds among an international Delphi consensus panel called IMPACT, sponsored by the French National Research Agency. The consensus panel included 11 international experts from France, Switzerland, and Canada.

Results

Based on the available evidence, olfaction is the most long-lasting impairment found in PACS patients. However, while olfaction is the most prevalent impairment, expert consensus statements recommend that AD olfactory screening should not be used on patients with a history of PACS at this point in time. Experts recommend that olfactory screenings can only be recommended once those under study have reported full recovery. This is particularly important for the deployment of the olfactory identification subdimension. The expert assessment that more long-term studies are needed after a period of full recovery, suggests that this consensus statement requires an update in a few years.

Conclusion

Based on available evidence, olfaction could be long-lasting in PACS patients. However, according to expert consensus statements, AD olfactory screening is not recommended for patients with a history of PACS until complete recovery has been confirmed in the literature, particularly for the identification sub-dimension. This consensus statement may require an update in a few years.

A screening method for cervical myelopathy using machine learning to analyze a drawing behavior

Early detection of cervical myelopathy (CM) is important for a favorable outcome, as its prognosis is poor when left untreated. We developed a screening method for CM using machine learning-based analysis of the drawing behavior of 38 patients with CM and 66 healthy volunteers. Using a stylus pen, the participants traced three different shapes displayed on a tablet device. During the tasks, writing behaviors, such as the coordinates, velocity, and pressure of the stylus tip, along with the drawing time, were recorded. From these data, features related to the drawing pressure, and time to trace each shape and combination of shapes were used as training data for the support vector machine, a machine learning algorithm. To evaluate the accuracy, a receiver operating characteristic curve was generated, and the area under the curve (AUC) was calculated. Models with triangular waveforms tended to be the most accurate. The best triangular wave model identified patients with and without CM with 76% sensitivity and 76% specificity, yielding an AUC of 0.80. Our model was able to classify CM with high accuracy and could be applied to the development of disease screening systems useful outside the hospital setting.

Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions

The analysis of functional upper extremity (UE) movement kinematics has implications across domains such as rehabilitation and evaluating job-related skills. Using movement kinematics to quantify movement quality and skill is a promising area of research but is currently not being used widely due to issues associated with cost and the need for further methodological validation. Recent developments by computationally-oriented research communities have resulted in potentially useful methods for evaluating UE function that may make kinematic analyses easier to perform, generally more accessible, and provide more objective information about movement quality, the importance of which has been highlighted during the COVID-19 pandemic. This narrative review provides an interdisciplinary perspective on the current state of computer-assisted methods for analyzing UE kinematics with a specific focus on how to make kinematic analyses more accessible to domain experts. We find that a variety of methods exist to more easily measure and segment functional UE movement, with a subset of those methods being validated for specific applications. Future directions include developing more robust methods for measurement and segmentation, validating these methods in conjunction with proposed kinematic outcome measures, and studying how to integrate kinematic analyses into domain expert workflows in a way that improves outcomes.

A study of auxiliary screening for Alzheimer’s disease based on handwriting characteristics

Background and objectives

Alzheimer’s disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and normal elderly people to model handwriting characteristics. The aim is to investigate whether handwriting analysis has a promising future in AD auxiliary screening or even auxiliary diagnosis and to provide a basis for developing a handwriting-based diagnostic tool.

Materials and methods

Thirty-four AD patients (15 males, 77.15 ± 1.796 years) and 45 healthy controls (20 males, 74.78 ± 2.193 years) were recruited. Participants performed four writing tasks with digital dot-matrix pens which simultaneously captured their handwriting as they wrote. The writing tasks consisted of two graphics tasks and two textual tasks. The two graphics tasks are connecting fixed dots (task 1) and copying intersecting pentagons (task 2), and the two textual tasks are dictating three words (task 3) and copying a sentence (task 4). The data were analyzed by using Student’s t-test and Mann–Whitney U test to obtain statistically significant handwriting characteristics. Moreover, seven classification algorithms, such as eXtreme Gradient Boosting (XGB) and Logistic Regression (LR) were used to build classification models. Finally, the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Area Under Curve (AUC) were used to assess whether writing scores and kinematics parameters are diagnostic.

Results

Kinematic analysis showed statistically significant differences between the AD and controlled groups for most parameters (p < 0.05, p < 0.01). The results found that patients with AD showed slower writing speed, tremendous writing pressure, and poorer writing stability. We built statistically significant features into a classification model, among which the model built by XGB was the most effective with a maximum accuracy of 96.55%. The handwriting characteristics also achieved good diagnostic value in the ROC analysis. Task 2 had a better classification effect than task 1. ROC curve analysis showed that the best threshold value was 0.084, accuracy = 96.30%, sensitivity = 100%, specificity = 93.41%, PPV = 92.21%, NPV = 100%, and AUC = 0.991. Task 4 had a better classification effect than task 3. ROC curve analysis showed that the best threshold value was 0.597, accuracy = 96.55%, sensitivity = 94.20%, specificity = 98.37%, PPV = 97.81%, NPV = 95.63%, and AUC = 0.994.

Conclusion

This study’s results prove that handwriting characteristic analysis is promising in auxiliary AD screening or AD diagnosis.

Characteristics of Drawing Process Differentiate Alzheimer’s Disease and Dementia with Lewy Bodies

Background: Early differential diagnosis of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been extensively studied. Objective: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB. Methods: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses. Results: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD. Conclusion: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB.

On Extracting Digitized Spiral Dynamics’ Representations: A Study on Transfer Learning for Early Alzheimer’s Detection

This work proposes a decision-aid tool for detecting Alzheimer’s disease (AD) at an early stage, based on the Archimedes spiral, executed on a Wacom digitizer. Our work assesses the potential of the task as a dynamic gesture and defines the most pertinent methodology for exploiting transfer learning to compensate for sparse data. We embed directly in spiral trajectory images, kinematic time functions. With transfer learning, we perform automatic feature extraction on such images. Experiments on 30 AD patients and 45 healthy controls (HC) show that the extracted features allow a significant improvement in sensitivity and accuracy, compared to raw images. We study at which level of the deep network features have the highest discriminant capabilities. Results show that intermediate-level features are the best for our specific task. Decision fusion of experts trained on such descriptors outperforms low-level fusion of hybrid images. When fusing decisions of classifiers trained on the best features, from pressure, altitude, and velocity images, we obtain 84% of sensitivity and 81.5% of accuracy, achieving an absolute improvement of 22% in sensitivity and 7% in accuracy. We demonstrate the potential of the spiral task for AD detection and give a complete methodology based on off-the-shelf features.

Automated Early Detection of Alzheimer's Disease by Capturing Impairments in Multiple Cognitive Domains with Multiple Drawing Tasks

BACKGROUND

Automatic analysis of the drawing process using a digital tablet and pen has been applied to successfully detect Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most studies focused on analyzing individual drawing tasks separately, and the question of how a combination of drawing tasks could improve the detection performance thus remains unexplored.

OBJECTIVE

We aimed to investigate whether analysis of the drawing process in multiple drawing tasks could capture different, complementary aspects of cognitive impairments, with a view toward combining multiple tasks to effectively improve the detection capability.

METHODS

We collected drawing data from 144 community-dwelling older adults (27 AD, 65 MCI, and 52 cognitively normal, or CN) who performed five drawing tasks. We then extracted motion- and pause-related drawing features for each task and investigated the statistical associations of the features with the participants' diagnostic statuses and cognitive measures.

RESULTS

The drawing features showed gradual changes from CN to MCI and then to AD, and the changes in the features for each task were statistically associated with cognitive impairments in different domains. For classification into the three diagnostic categories, a machine learning model using the features from all five tasks achieved a classification accuracy of 75.2%, an improvement by 7.8% over that of the best single-task model.

CONCLUSION

Our results demonstrate that a common set of drawing features from multiple drawing tasks can capture different, complementary aspects of cognitive impairments, which may lead to a scalable way to improve the automated detection of AD and MCI.

Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset

Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL—0.65 (HF), 0.58 (CNN); LOLO—0.65 (HF), 0.57 (CNN); and ALC—0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL—0.66 (HF), 0.62 (CNN); LOLO—0.56 (HF), 0.54 (CNN); and ALC—0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6% (mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN).

Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets

BACKGROUND

With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations.

OBJECTIVE

The aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations.

METHODS

We collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning-based regression analyses.

RESULTS

We found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen's horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2=0.35; permutation test, P<.001).

CONCLUSIONS

This study presents initial empirical evidence of the capability of automated analysis of the drawing process as an estimator of global cognition that is applicable across populations. Our results suggest that such automated analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment.

A survey of visual and procedural handwriting analysis for neuropsychological assessment

To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed handwriting representation and estimation methods presented in the literature and discusses their strengths and weaknesses. It also highlights the limitations of existing processes and the challenges commonly faced when designing such systems. High-level directions for further research conclude the paper.

Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson's Disease

Parkinson's disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative movement disorders associated with cognitive dysfunction. The Luria's Alternating Series Test (LAST) is a clinical tool sensitive to both graphomotor problems and perseverative tendencies that may suggest the dysfunction of prefrontal and/or frontostriatal areas and may be used in PD and PSP assessment. It requires the participant to draw a series of alternating triangles and rectangles. In the study, two clinical groups-51 patients with PD and 22 patients with PSP-were compared to 32 neurologically intact seniors. Participants underwent neuropsychological assessment. The LAST was administered in a paper and pencil version, then scanned and preprocessed. The series was automatically divided into characters, and the shapes were recognized as rectangles or triangles. In the feature extraction step, each rectangle and triangle was regarded both as an image and a two-dimensional signal, separately and as a part of the series. Standard and novel features were extracted and normalized using characters written by the examiner. Out of 71 proposed features, 51 differentiated the groups (p < 0.05). A classifier showed an accuracy of 70.5% for distinguishing three groups.

Forensic examination of effects of Parkinsonism on various handwriting characteristics

Parkinsonism is a neurodegenerative syndrome that causes impairment of motor skills in affected persons. Thus, adverse effects may be produced in the handwriting of persons suffering from Parkinsonism. Medication used for the treatment of Parkinsonism is known to subside certain motor defects for specific time intervals, showing slight differences or improvement in certain handwriting characteristics during those intervals on the same day as compared to the ones executed before medication. Certain handwriting characteristics affected due to Parkinsonism may be mistaken as forged features due to poor line quality, which can cause suspicion upon the authenticity of important legal documents. The present research work has been carried out to determine the effects of Parkinsonism and medication used for its treatment on handwriting. Handwriting/signature samples executed before and after the onset of Parkinsonism (both pre- and post-medication) have been randomly collected from 70 participants. These handwritings have been evaluated separately and compared inter-se for various handwriting characteristics with qualitative and statistical approach. The results have demonstrated significant changes in most of the characteristics in both affected writings of majority of participants as compared to their corresponding earlier writings. Thus, forensic document experts should be aware of the detrimental effects of Parkinsonism on handwriting in pre- and post-medication conditions of this ailment.

A Novel Computer Vision Approach to Kinematic Analysis of Handwriting with Implications for Assessing Neurodegenerative Diseases

Fine motor movement is a demonstrated biomarker for many health conditions that are especially difficult to diagnose early and require sensitivity to change in order to monitor over time. This is particularly relevant for neurodegenerative diseases (NDs), including Parkinson's Disease (PD) and Alzheimer's Disease (AD), which are associated with early changes in handwriting and fine motor skills. Kinematic analysis of handwriting is an emerging method for assessing fine motor movement ability, with data typically collected by digitizing tablets; however, these are often expensive, unfamiliar to patients, and are limited in the scope of collectible data. In this paper, we present a vision-based system for the capture and analysis of handwriting kinematics using a commodity camera and RGB video. We achieve writing position estimation within 0.5 mm and speed and acceleration errors of less than 1.1%. We further demonstrate that this data collection process can be part of an ND screening system with a developed ensemble classifier achieving 74% classification accuracy of Parkinson's Disease patients with vision-based data. Overall, we demonstrate that this approach is an accurate, accessible, and informative alternative to digitizing tablets and with further validation has potential uses in early disease screening and long-term monitoring.Clinical relevance- This work establishes a more accessible alternative to digitizing tablets for extracting handwriting kinematic data through processing of RGB video data captured by commodity cameras, such as those in smartphones, with computer vision and machine learning. The collected data has potential for use in analysis to objectively and quantitatively differentiate between healthy individuals and patients with NDs, including AD and PD, as well as other diseases with biomarkers displayed in fine motor movement. The developed system has potential applications including providing widespread screening systems for NDs in low-income areas and resource-poor health systems, as well as an accessible form of disease long-term monitoring through telemedicine.

Sit-to-Stand Test for Neurodegenerative Diseases Video Classification

In this extended version of this paper, an automatic video diagnosis system for dementia classification is presented. Starting from video recordings of patients and control subjects, performing sit-to-stand test, the designed system is capable of extracting relevant patterns for binary discern patients with dementia from healthy subjects. The original system achieved an accuracy 0.808 by using the rigorous inter-patient separation scheme especially suited for medical purposes. This separation scheme provides the use of some people for training and others, different, people for testing. The implementation of features from the kinematic theory of rapid human movement and its sigma-lognormal model together with classic features increased the overall accuracy of the system to 0.947 F1 score. In addition, multi-class classification was performed with the aim of classifying neurodegenerative disease severities. This work is an original and pioneering work on sit-to-stand video classification for neurodegenerative diseases, its novelties are on phases segmentation, experimental setup and the application of kinematic theory of rapid human movements to sit-to-stand videos for neurodegenerative disease assessment.

Screening of Parkinson's Disease Using Geometric Features Extracted from Spiral Drawings

Conventional means of Parkinson’s Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).

The Accuracy of a Screening System for Carpal Tunnel Syndrome Using Hand Drawing

When carpal tunnel syndrome (CTS), an entrapment neuropathy, becomes severe, thumb motion is reduced, which affects manual dexterity, such as causing difficulties in writing; therefore, early detection of CTS by screening is desirable. To develop a screening method for CTS, we developed a tablet app to measure the stylus trajectory and pressure of the stylus tip when drawing a spiral on a tablet screen using a stylus and, subsequently, used these data as training data to predict the classification of participants as non-CTS or CTS patients using a support vector machine. We recruited 33 patients with CTS and 31 healthy volunteers for this study. From our results, non-CTS and CTS were classified by our screening method with 82% sensitivity and 71% specificity. Our CTS screening method can facilitate the screening for potential patients with CTS and provide a quantitative assessment of CTS.

Primary Progressive Aphasia: Use of Graphical Markers for an Early and Differential Diagnosis

Primary progressive aphasia (PPA) brings together neurodegenerative pathologies whose main characteristic is to start with a progressive language disorder. PPA diagnosis is often delayed in non-specialised clinical settings. With the technologies' development, new writing parameters can be extracted, such as the writing pressure on a touch pad. Despite some studies having highlighted differences between patients with typical Alzheimer's disease (AD) and healthy controls, writing parameters in PPAs are understudied. The objective was to verify if the writing pressure in different linguistic and non-linguistic tasks can differentiate patients with PPA from patients with AD and healthy subjects. Patients with PPA (n = 32), patients with AD (n = 22) and healthy controls (n = 26) were included in this study. They performed a set of handwriting tasks on an iPad® digital tablet, including linguistic, cognitive non-linguistic, and non-cognitive non-linguistic tasks. Average and maximum writing pressures were extracted for each task. We found significant differences in writing pressure, between healthy controls and patients with PPA, and between patients with PPA and AD. However, the classification of performances was dependent on the nature of the tasks. These results suggest that measuring writing pressure in graphical tasks may improve the early diagnosis of PPA, and the differential diagnosis between PPA and AD.

Handwriting graphical parameters analysis in Posterior Cortical Atrophy: A case report

Posterior Cortical Atrophy (PCA) is a rare neurodegenerative syndrome characterized by an occipital atrophy resulting in a progressive impairment of upper visual functions. The inconsistency of terminology of this pathology makes its diagnosis difficult and delayed. We present a 76-year-old patient with PCA having difficulties in reading, writing, and daily manipulations. The objective was to evaluate the kinematic writing parameters. Linguistic, cognitive-non-linguistic and non-cognitive-non-linguistic graphical tasks were performed. The kinematic parameters extracted were jerk, velocity and pressure. We found a kinematic profile for all these parameters different from what observed in healthy controls and patients with Alzheimer's Disease. This study, through an analysis of writing features never studied before in PCA, shows the interest of handwriting kinematic analysis in the clinical diagnosis of PCA.

An improved sex-specific and age-dependent classification model for Parkinson's diagnosis using handwriting measurement

BACKGROUND AND OBJECTIVES

Diagnosis of Parkinson's with higher accuracy is always desirable to slow down the progression of the disease and improved quality of life. There are evidences of inherent neurological differences between male and females as well as between elderly and adults. However, the potential of such gender and age infomration have not been exploited yet for Parkinson's identification.

METHODS

In this paper, we develop a sex-specific and age-dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from individuals with Parkinson's (n = 37; m/f-19/18;age-69.3 ± 10.9yrs) and healthy controls (n = 38; m/f-20/18;age-62.4 ± 11.3yrs). A support vector machine ranking method is used to present the features specific to their dominance in sex and age group for Parkinson's diagnosis.

RESULTS

The sex-specific and age-dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75% (SD = 1.63) with the female-specific classifier, and 79.55% (SD = 1.58) with the old-age dependent classifier was observed in comparison to 75.76% (SD = 1.17) accuracy with the generalized classifier.

CONCLUSIONS

Combining the age and sex information proved to be encouraging in classification. A distinct set of features were observed to be dominating for higher classification accuracy in a different category of classification.

A novel approach combining temporal and spectral features of Arabic online handwriting for Parkinson's disease prediction

BACKGROUND

Parkinson's disease (PD) affects millions of people worldwide, and it is predicted that this pathology will gravely increase in the next few years. Unfortunately, there's currently no cure for this disease, indeed an early diagnosis of Parkinson's disease can help to better manage its symptoms and its evolution. One of the most frequent abilities and usually also the first manifestation of Parkinson's disease is alteration of handwriting.

NEW METHOD

We propose a novel method to detect Parkinson's disease, based on the segmentation of the online handwritten text into lines. Indeed, we propose to compare Parkinson's disease patients and healthy controls, based on the full dynamics of new temporal and spectral features. Three classifiers were used, K-Nearest Neighbors, Support Vector Machine and Decision Trees. The performances of these three classifiers were estimated using a stratified nested 10 cross-validation. All the models in this study have been evaluated using classification accuracy, balanced accuracy, sensitivity, specificity, F-Score and Matthews Correlation Coefficient.

RESULTS

An accuracy of 92.86 % was obtained with Decision Trees classifier in the last line. The new categories of spectral and temporal features gave the best classification performances in comparison to the basic statistical features.

COMPARISON WITH EXISTING METHODS

Previous studies have only focused on words or sentences. This is the first study to deal with the analysis of a text composed of several lines.

CONCLUSION

The last line discriminates at best between Parkinson's disease patients and healthy controls. This obtained result has further strengthened our hypothesis concerning the fatigue occurring while writing in PD patients.

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks

Nowadays, an important research effort in healthcare biometrics is finding accurate biomarkers that allow developing medical-decision support tools. These tools help to detect and supervise illnesses like Parkinson’s disease (PD). This paper contributes to this effort by analyzing a convolutional neural network (CNN) for PD detection from drawing movements. This CNN includes two parts: feature extraction (convolutional layers) and classification (fully connected layers). The inputs to the CNN are the module of the Fast Fourier’s transform in the range of frequencies between 0 Hz and 25 Hz. We analyzed the discrimination capability of different directions during drawing movements obtaining the best results for X and Y directions. This analysis was performed using a public dataset: Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet dataset. The best results obtained in this work showed an accuracy of 96.5%, a F1-score of 97.7%, and an area under the curve of 99.2%.

eHealth and Artificial Intelligence

Artificial intelligence is changing the healthcare industry from many perspectives: diagnosis, treatment, and follow-up. A wide range of techniques has been proposed in the literature. In this special issue, 13 selected and peer-reviewed original research articles contribute to the application of artificial intelligence (AI) approaches in various real-world problems. Papers refer to the following main areas of interest: feature selection, high dimensionality, and statistical approaches; heart and cardiovascular diseases; expert systems and e-health platforms.