Determining the Presence of Bias Error Using Statistical Methods

Current techniques in image-guided surgery rely on the use of localizers for the measurement of position in physical space. These measurements are prone to error due to intrinsic properties of the localizer used. The error and thus accuracy of a localizer can be determined using various techniques, many of which assume that the error is isotropic and free of bias. A bias error adds an orientation dependence to the error of measured points. Determination of the presence of a bias error is an important component in the characterization of a localizer's performance. Statistical analysis of localized points on a rigid phantom can be used to detect the presence of a bias error. In this paper, we will examine the use of statistical techniques in the characterization of a series of localizers and how that information is useful in determining localizer efficacy.     

The Effect of Measurement Errors on Correlation Estimates in Spike-Interval Sequences

The serial correlogram (SCG) is used to determine the amount of dependence in spike-interval sequences. This communication demonstrates the effects of measurement errors [i.e., false alarms (FA's) and false dismissals (FD's) in spike-detection algorithms] on correlation coefficients when the sequence has a practical sample size.     

Factors in automated gait evaluation

A summary is given of measurement modalities, which set some of the framework for feasibility of automated gait evaluation and dictate some of the modeling and analysis that must be undertaken. The discussion covers electromyography, kinematics, ground reaction forces, and dynamics. Specific gait evaluation problems are discussed, and the state of the art and future directions are examined.     

Quantitative representation of electromyographic patterns generatedduring human locomotion

The problem addressed is the representation of electromyographic (EMG) profiles in an economical manner so that two tasks can be undertaken: (1) discovery of the different types of patterns in a population using pattern analysis techniques and (2) integration of the information in the EMG profiles with kinematic and dynamic information. The three signal processing techniques used to extract representative features of the EMG profiles are summarized. These techniques are based on the Tauberian approximation, Fourier series, and Karhunen-Loeve expansion and were chosen because they emphasize some characteristic of the gait EMG. All are designed to reduce the dimensionality of the linear envelope of the EMG so that interpretability can be enhanced and subsequent quantitative analyses can be performed.     

Acoustical properties of speech as indicators of depression and suicidal risk

Acoustic properties of speech have previously been identified as possible cues to depression, and there is evidence that certain vocal parameters may be used further to objectively discriminate between depressed and suicidal speech. Studies were performed to analyze and compare the speech acoustics of separate male and female samples comprised of normal individuals and individuals carrying diagnoses of depression and high-risk, near-term suicidality. The female sample consisted of ten control subjects, 17 dysthymic patients, and 21 major depressed patients. The male sample contained 24 control subjects, 21 major depressed patients, and 22 high-risk suicidal patients. Acoustic analyses of voice fundamental frequency (Fo), amplitude modulation (AM), formants, and power distribution were performed on speech samples extracted from audio recordings collected from the sample members. Multivariate feature and discriminant analyses were performed on feature vectors representing the members of the control and disordered classes. Features derived from the formant and power spectral density measurements were found to be the best discriminators of class membership in both the male and female studies. AM features emerged as strong class discriminators of the male classes. Features describing Fo were generally ineffective discriminators in both studies. The results support theories that identify psychomotor disturbances as central elements in depression and suicidality.     

Changes in electromyographic gait patterns of calf muscles with walking speed

The electromyographic gait patterns of normal individuals change with walking speed. The various pattern types are determined using factor and cluster analyses, and the speeds associated with each type are compared using analysis of variance.     

Temporal feature extraction and clustering analysis ofelectromyographic linear envelopes in gait studies

A technique for automatically clustering linear envelopes of the EMG during gait has been developed which uses a temporal feature representation and a maximum peak matching scheme. This new technique provides a viable way to define compact and meaningful EMG waveform features. The envelope matching is performed by dynamic programming, providing qualitatively the largest numbers of matched peaks and quantitatively a minimum distance measurement. The resulting averaged EMG profiles have low statistical variation and can serve as templates for EMG comparison and further classification.     

Gaitspert: An Expert System for the Evaluation of Abnormal Human Locomotion Arising from Stroke

This paper describes the implementation and use of a knowledge-based system for the evaluation of abnormal human locomotion arising from cerebral vascular accidents (CVA). An expert system (GAITSPERT) that is knowledgeable about the evaluation of human gait has been implemented primarily as a clinical tool that can produce assessments of underlying stroke disease states and recommendations about therapeutic interventions. GAITSPERT combines artificial intelligence (AI) analysis methodologies with biomedical signal acquisition and evaluation providing intelligent processing of kinematic, electromyographic (EMG), and foot-switch data. GAITSPERT was constructed using GENIE, a general-purpose knowledge-enginering tool, and a knowledge base of stroke-related facts about neuromuscular disabilities.     

Automated Extraction of Activity Features in Linear Envelopes of Locomotor Electromyographic Patterns

For the envelope of the electromyographic activity recorded during locomotion, a feature extraction scheme has been developed. It recognizes the time, duration, and amplitude of phases of activity. The method is based on the Tauberian approximation for modeling waveforms as a sum of identically shaped pulses with different time delays and amplitudes. Initial conditions on the pulse properties are set and an optimal solution is sought. Any multiple pulses that model one phase of activity are then combined into one equivalent pulse. The method is completely automated; thus, it operates without any user intervention.