arXiv subjects

Nicolai Ladegaard

Publications and source records attributed to Nicolai Ladegaard.

3 recordsLinked to original sources

arXiv2023

Real-time unobtrusive sleep monitoring of in-patients with affective disorders: a feasibility study

Sleep and mental health are highly related concepts, and it is an important research and clinical priority to understand their interactions. In-bed sensors using ballistocardiography provide the possibility of unobtrusive measurements of sleep. In this study, we examined the feasibility of ballistocardiography in measuring key aspects of sleep in psychiatric in-patients. Specifically, we examined a sample of patients diagnosed with depression and bipolar disorder. The subjective experiences of the researchers conducting the study are explored and descriptive analyses of patient sleep are subsequently presented. The practicalities of using the ballistocardiography device seem to be favourable. There were no apparent issues regarding data quality or data integrity. Of clinical interest, we found no link between length of stay and reduced time in bed (b = -0.06, SE = 0.03, t = -1.76, p = .08). Using ballistocardiography for measurements on in-patients with affective disorders seems to be a feasible approach.

eess.SP↗

arXiv2023

Measuring Cognitive Abilities in the Wild: Validating a Population-Scale Game-Based Cognitive Assessment

Rapid individual cognitive phenotyping holds the potential to revolutionize domains as wide-ranging as personalized learning, employment practices, and precision psychiatry. Going beyond limitations imposed by traditional lab-based experiments, new efforts have been underway towards greater ecological validity and participant diversity to capture the full range of individual differences in cognitive abilities and behaviors across the general population. Building on this, we developed Skill Lab, a novel game-based tool that simultaneously assesses a broad suite of cognitive abilities while providing an engaging narrative. Skill Lab consists of six mini-games as well as 14 established cognitive ability tasks. Using a popular citizen science platform (N = 10725), we conducted a comprehensive validation in the wild of a game-based cognitive assessment suite. Based on the game and validation task data, we constructed reliable models to simultaneously predict eight cognitive abilities based on the users' in-game behavior. Follow-'-up validation tests revealed that the models can discriminate nuances contained within each separate cognitive ability as well as capture a shared main factor of generalized cognitive ability. Our game-based measures are five times faster to complete than the equivalent task-based measures and replicate previous findings on the decline of certain cognitive abilities with age in our large cross-sectional population sample (N = 6369). Taken together, our results demonstrate the feasibility of rapid in-the-wild systematic assessment of cognitive abilities as a promising first step towards population-scale benchmarking and individualized mental health diagnostics.

physics.soc-ph↗

arXiv2023

Automated speech- and text-based classification of neuropsychiatric conditions in a multidiagnostic setting

Speech patterns have been identified as potential diagnostic markers for neuropsychiatric conditions. However, most studies only compare a single clinical group to healthy controls, whereas clinical practice often requires differentiating between multiple potential diagnoses (multiclass settings). To address this, we assembled a dataset of repeated recordings from 420 participants (67 with major depressive disorder, 106 with schizophrenia and 46 with autism, as well as matched controls), and tested the performance of a range of conventional machine learning models and advanced Transformer models on both binary and multiclass classification, based on voice and text features. While binary models performed comparably to previous research (F1 scores between 0.54-0.75 for autism spectrum disorder, ASD; 0.67-0.92 for major depressive disorder, MDD; and 0.71-0.83 for schizophrenia); when differentiating between multiple diagnostic groups performance decreased markedly (F1 scores between 0.35-0.44 for ASD, 0.57-0.75 for MDD, 0.15-0.66 for schizophrenia, and 0.38-0.52 macro F1). Combining voice and text-based models yielded increased performance, suggesting that they capture complementary diagnostic information. Our results indicate that models trained on binary classification may learn to rely on markers of generic differences between clinical and non-clinical populations, or markers of clinical features that overlap across conditions, rather than identifying markers specific to individual conditions. We provide recommendations for future research in the field, suggesting increased focus on developing larger transdiagnostic datasets that include more fine-grained clinical features, and that can support the development of models that better capture the complexity of neuropsychiatric conditions and naturalistic diagnostic assessment.

cs.CL↗