There is growing interest in developing reliable, non-invasive, and cost-effective methods for early diagnosis of neurodegenerative diseases such as Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). In this regard, handwriting-based tasks have shown potential in differentiating MCI and AD patients from healthy controls (HCs). However, previous work has reported mixed results when using different symbols and data representations. We address this research gap by developing computational models (convolutional and recurrent neural networks) to differentiate MCI and AD from HCs with off-line (scanned images) and on-line (discrete time series) house drawings. Notably, we observed that augmenting on-line data and then converting it to off-line format, a method we refer to as "OnOff-line", yielded the best performance results in binary classification tasks. These findings highlight the effectiveness of on-line representations in capturing handwriting dynamics more accurately. Ultimately, our work opens new avenues for future research to enhance automated diagnostic of MCI and AD from handwriting analysis.

Alzheimer's disease (AD) is a cognitive disorder, marked by memory loss and impaired reasoning, that requires early detection methods to better manage and potentially slow down the disease's progression. Recent advances in machine learning have offered new possibilities for AD detection using handwriting analysis, however previous work has considered only one type of input source, e.g. clock or pentagon drawings. Here we propose to develop an efficient method for detecting AD's early symptoms using Deep Feature Concatenation (DFC) models considering multiple handwriting sources: pentagon drawings, self-reported sentences, and signatures. Substantial performance improvements were observed when considering all input sources together with data augmentation techniques. For example, classification accuracy increased from 60% (best model, without data augmentation) to 80% (DFC and data augmentation). Our findings show that the use of diverse input sources can lead to an efficient and cost-effective method for early AD detection. Looking forward into the future, our study highlights the potential of DFC in supporting home-based healthcare diagnoses which is a crucial step in integrating artificial intelligence into healthcare practices.