Biomedical subjects

Martti Juhola

Publications and source records attributed to Martti Juhola.

3 recordsLinked to original sources

PubMed2003

Lossy compression of eye movement and auditory brainstem response signals.

Eye movement and auditory brainstem response signals recorded for balance and hearing investigations were used as a medical test battery for several types of lossy compression techniques. These signals are associated with the function of the ears. The former signals are used to assess the balance problems (especially vertigo) of a subject and the latter his or her hearing problems. New technique is also presented based on successive approximation quantization. The effect of information loss on medical parameters computed from the signals in the course of compression was evaluated for brainstem response signals. It is important to ensure that lossy compression techniques of these biomedical signals do not impair medical parameter values computed from the signals.

Algorithms↗

PubMed2003

Development of virtual reality stimuli for force platform posturography.

People relying much on vision in the control of posture are known to have an elevated risk of falling. Dependence on visual control is an important parameter in the diagnosis of balance disorders. We have previously shown that virtual reality (VR) methods can be used to produce visual stimuli that affect balance, but suitable stimuli need to be found. In this study, the effect of six different VR stimuli on the balance of 22 healthy test subjects was evaluated using force platform posturography. We report in more detail and expand the results published earlier. According to the tests two of the stimuli have a significant destabilizing effect on balance. In addition a significant displacement effect on the subject's center of pressure (COP) was found. Thus it is shown that the design of VR stimuli to cause different effects on the control of balance is possible.

Adult↗

PubMed2002

Generating decision trees from otoneurological data with a variable grouping method.

When medical data sets are modelled by machine learning methods, wealth of variables may be available. This paper deals with variable selection for decision tree induction in the context of two otoneurological data sets: vertigo data, and postoperative nausea and vomiting data. First, a variable grouping method based on measures of association and graph theoretic techniques was used to gain insight into data. Then, representations of learning data were defined using the information from discovered variable groups, and decision trees were generated. The use of variable grouping method was beneficial by revealing interesting associations between variables and enabling generation of accurate and reasonable decision trees that modelled the application areas from different viewpoints.

Data Collection↗