Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation

Motivation: Circadian rhythms are prevalent in most organisms. Identification of circadian-regulated genes is a crucial step in discovering underlying pathways and processes that are clock-controlled. Such genes are largely detected by searching periodic patterns in microarray data. However, temporal gene expression profiles usually have a short time-series with low sampling frequency and high levels of noise. This makes circadian rhythmic analysis of temporal microarray data very challenging

Personalised profiling to identify clinically relevant changes in tremor due to multiple sclerosis

Background: There is growing interest in sensor-based assessment of upper limb tremor in multiple sclerosis and other movement disorders. However, previously such assessments have not been found to offer any improvement over conventional clinical observation in identifying clinically relevant changes in an individual's tremor symptoms, due to poor test-retest repeatability. Method: We hypothesised that this barrier could be overcome by constructing a tremor change metric that is customised to each individual's tremor characteristics, such that random variability can be distinguished from clinically relevant changes in symptoms. In a cohort of 24 people with tremor due to multiple sclerosis, the newly proposed metrics were compared against conventional clinical and sensor-based metrics. Each metric was evaluated based on Spearman rank correlation with two reference metrics extracted from the Fahn-Tolosa-Marin Tremor Rating Scale: a task-based measure of functional disability (FTMTRS B) and the subject's self-assessment of the impact of tremor on their activities of daily living (FTMTRS C). Results: Unlike the conventional sensor-based and clinical metrics, the newly proposed ’change in scale’ metrics presented statistically significant correlations with changes in self-assessed impact of tremor (max R2>0.5,p< 0.05 after correction for false discovery rate control). They also outperformed all other metrics in terms of correlations with changes in task-based functional performance (R2=0.25 vs. R2=0.15 for conventional clinical observation, both p< 0.05).Conclusions: The proposed metrics achieve an elusive goal of sensor-based tremor assessment: improving on conventional visual observation in terms of sensitivity to change. Further refinement and evaluation of the proposed techniques is required, but our core findings imply that the main barrier to translational impact for this application can be overcome. Sensor-based tremor assessments may improve personalised treatment selection and the efficiency of clinical trials for new treatments by enabling greater standardisation and sensitivity to clinically relevant changes in symptoms

The recording and analysis of tremor in neurological disorders

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN025773 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Classification of normal and pathological tremors using a multidimensional electromagnetic system.

A new multidimensional movement analysis system was used to record limb tremor over six degrees-of-freedom, and signal processing techniques were explored to develop a suitable classification method to distinguish between different types of tremor. The specific aims were to investigate the ability of the system to screen for differences between normal subjects and a group of neurological patients, and then to differentiate between three diagnostic groups of patients. Postural tremor at the hand was recorded in normal subjects (n=24) and patients with essential tremor (n=21), multiple sclerosis (n=17) and parkinsonism (n=19). Data were collected using a 3Space Fastrak((R)) (Polhemus, Inc.) over six degrees-of-freedom (three translational directions and three rotations). Spectral estimates produced measures of tremor frequency and amplitude. Mathematical models of the data, using autoregressive modelling and K-nearest neighbour classification, produced parameters used to classify, (1) the normal subjects and 24 patients (using the three rotational movements), and (2) the three patient groups (using all six movement directions). Results were given in terms of the probability of each subject belonging to the groups being classified. 70%). The diagnostic classification produced clear differences between the patient groups (60% for essential tremor, 80% for multiple sclerosis and 60% for parkinsonism). The ability of this assessment technique to distinguish between postural tremor in normal subjects and neurological patients suggests that it could be developed as a screening tool. Classification of tremors between the patients groups, with a high degree of sensitivity, indicates the potential for further development of the system as a diagnostic aid

Classification of normal and pathological tremors using a multidimensional electromagnetic system.

A new multidimensional movement analysis system was used to record limb tremor over six degrees-of-freedom, and signal processing techniques were explored to develop a suitable classification method to distinguish between different types of tremor. The specific aims were to investigate the ability of the system to screen for differences between normal subjects and a group of neurological patients, and then to differentiate between three diagnostic groups of patients. Postural tremor at the hand was recorded in normal subjects (n=24) and patients with essential tremor (n=21), multiple sclerosis (n=17) and parkinsonism (n=19). Data were collected using a 3Space Fastrak((R)) (Polhemus, Inc.) over six degrees-of-freedom (three translational directions and three rotations). Spectral estimates produced measures of tremor frequency and amplitude. Mathematical models of the data, using autoregressive modelling and K-nearest neighbour classification, produced parameters used to classify, (1) the normal subjects and 24 patients (using the three rotational movements), and (2) the three patient groups (using all six movement directions). Results were given in terms of the probability of each subject belonging to the groups being classified. 70%). The diagnostic classification produced clear differences between the patient groups (60% for essential tremor, 80% for multiple sclerosis and 60% for parkinsonism). The ability of this assessment technique to distinguish between postural tremor in normal subjects and neurological patients suggests that it could be developed as a screening tool. Classification of tremors between the patients groups, with a high degree of sensitivity, indicates the potential for further development of the system as a diagnostic aid