Detecting the effect of Alzheimer s disease on everyday motion behavior

Transcription

1 Detecting the effect of Alzheimer s disease on everyday motion behavior Thomas Kirste 1, André Hoffmeyer 2, Alexandra Bauer 1, Susanne Schubert 1, and Stefan Teipel 1,2 1 University of Rostock, Germany; first.last@uni-rostock.de 2 German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; first.last@dzne.de Abstract. We report preliminary results of a study on the viability of accelerometric motion protocols of everyday behavior as biomarker for the in-vivo diagnosis of Alzheimer s disease. Based on data collected from 20 dyads (40 subjects) with one partner suffering from dementia and one healthy partner, we provide evidence that Alzheimer s disease manifests itself in changes of everyday behavior that are detectable in accelerometric behavior protocols. 1 Motivation Alzheimer s disease (AD) leads to significant changes in the temporal structure of activities. Abnormal motion behavior and degeneration of the sleep-waking cycle are among the most severe behavioral symptoms. An early detection and even a prediction of these behaviors would allow a timely onset of interventions that aim to delay the manifestation or exacerbation of symptoms and reduce the need of institutionalized care. Behavioral rating scales are the only diagnostic markers to detect the onset of abnormal motion behavior, but their reliability and accuracy is limited. Using behavioral cues as diagnostic instrument is interesting for two reasons: (i) data can be acquired in a person s everyday environment, (ii) once behavior analysis is in place, assistive functionality can be added for compensating errors in daily routine activities and for other forms of ambient assisted living. There is a number of projects aiming at establishing correlations between motion behavior and cognitive state see for instance [1,2,3]. However, it seems that current research focuses on studies with small numbers of test subjects, thus making it difficult to establish statistical models of motion behavior. In this paper, we present first results of an ongoing study that includes the data from 40 subjects. Aim of this study is to establish, whether motion protocols from persons diagnosed with AD exhibit changes that allow to discriminate them from motion protocols of healthy controls. We thereby focus on unlabeled motion protocols of unconstrained everyday behavior. As the structure of unconstrained everyday behavior is subject to a very large number of confounding factors concerning life style, physical fitness, health

2 state, etc., it is not clear that such a concept should work at all. However, the preliminary results we present in this paper give some evidence that this approach indeed may be viable. 2 Data Acquisition Data recording setup. We recruited 20 dyads (n = 40 subjects) with one partner diagnosed with AD and one partner with no cognitive abnormality detected (NAD); AD patients fulfilled the NINCDS-ADRDA criteria for clinically probable AD. Severity of cognitive impairment was assessed by a comprehensive set of instruments including the MMSE score [4] and the CERAD cognitive battery. The MMSE score serves as widely established measure for the severity of overall cognitive impairment. Subject selection has been balanced with respect to the variables Gender, Diagnosis, and Age. The average age of subjects was 74.4 (SD = 8.96), the overall average MMSE score was (SD = 7.02). Average MMSE score for AD subjects was 19.8 (SD = 7.6) and (SD = 1.53) for NAD subjects. It is important to note that none of the patients or care-givers reported severe behavioral symptoms in the patient. The partners in a dyad were asked to simultaneously record 50 continuous hours of everyday activity using an ankle-mounted accelerometric sensor (cf. Fig. 1). Recording sessions took place in the time frame of three days; in day 1 a medical student would attach the sensor, instruct the subjects on sensor handling, and collect the data on cognitive state. During day 3 Fig. 1. Physical sensor setup the student would again visit the dyad, collecting the sensors and the recorded data. This recording schedule guaranteed that we were able to record a complete day-night cycle for each subject (from 22:00 at day 1 to 22:00 at day 2). As sensor system we used the Shimmer product, providing a 3 axis accelerometer using Freescale MMA7260Q 1.5/2/4/6g MEMs devices giving 12 bits of resolution with sensor data storage to on-board MicroSD card. We used a sensor sampling rate of 50 Hz and a 4g value range. The average recording duration was 52 hours (SD =8.4 hours), resulting in an average number of samples in a subject s motion protocol. In total, 2088 hours of data were recorded, with a total volume of 8.86 GByte binary data. Preprocessing. Our underlying assumption for the analysis of motion protocols has been that the temporal structure of activity intensity correlates with a person s diagnosis as AD or NAD. As activity intensity, we considered the amplitude of the envelope of the carrier band in the range of 0.5 Hz to 5 Hz. The rationale behind the carrier band selection is the assumption that typical motion cycles (walking,... ) occur in roughly this frequency range. Higher frequencies include increasing levels of noise, lower frequencies begin to exhibit influences from gravitation. To isolate the activity envelope, we used the following detection method:

3 X024GFD1 : , 17: , 16:19 X024GMD0 : , 17: , 16:19 Acceleration Acceleration Fig. 2. Preprocessed data sample from a dyad. Left: female person, diagnosis AD (GFD1). Right: male person, diagnosis NAD (GMD0). X axis: recording hours, Y axis: activity level. Grey regions signify night (22:00 7:00). Label Duration Objective night.day 22:00 22:00 capture circadian activity cycle cal.day 24:00 24:00 check susceptibility of circadian cycle to temporal position night 22:00 05:00 check for effects of nightly unrest core.night 24:00 05:00 focus on deep night morning 05:00 12:00 morning activity afternoon 12:00 24:00 afternoon activity Table 1. Time windows used in spectral analysis First, we removed orientation information by computing the acceleration magnitude a m = a 2 x + a 2 y + a 2 z. To the acceleration magnitude signal we applied a sinc bandpass filter with lower band edge at 0.5 Hz and upper band edge at 5 Hz to remove gravitation and noise. Finally, we rectified the resulting signal and applied a sinc low pass. We experimented with two cutoff frequencies for the low pass filter, F1 = Hz and F2 = 0.25 Hz, corresponding to 40 sec. resp. 4 sec. temporal resolution for the activity envelope. Two examples for the output signal resulting from F1 are given in Fig Prediction Models Core objective of data analysis has been to classify persons as AD or NAD based on the activity envelopes. In addition, we were interested in the possibility of predicting the MMSE score from activity envelopes. Finally, in order to estimate the usefulness of activity envelopes for predicting other variables, we looked at models using a subject s gender as classification target. Spectral features. Our assumption is that the temporal structure of activity manifests itself in specific characteristics of the envelope s frequency spectrum. Therefore, we computed the Fourier transform for the envelopes, discarding the phase information and retaining the magnitude. We focused on the coefficients for wave numbers k =1..200, dropping the DC coefficient at k = 0 and ignoring all higher harmonics. As it was of interest to analyze whether there are specific

4 Target : Diagnosis Target : Gender accuracy M2 M3 M1 afternoon cal.day core.night morning night night.day afternoon cal.day core.night morning night night.day Fig. 3. Left: classification accuracy achieved for quadratic discriminant models using five pc-spectra and different time windows, for classification targets Diagnosis and Gender. (+): results for all subjects (n = 40). ( ): results without person A (n = 39). Right: Reconstructed motion profiles for the five pc-spectra selected for Model M1 sorted by magnitude of eigenvalues. X axis represents 24h. time windows within a day where AD-induced changes are especially visible, we performed the spectral analysis for the six different time windows in Table 1. As one of the test subjects, person A, had stopped the recording prematurely in the morning of day two, the analysis for the complete population of n = 40 subjects had to concentrate on the windows night and core.night. For a reduced subject set of size n = 39, all windows could be tested. From the set of subject spectra we computed the principal component spectra (pc-spectra) by principle component analysis. The idea is that pc-spectra identify characteristic general temporal activity structures from which daily routines are composed. For each subject, the factor loadings for the pc-spectra were computed. Classification models for Diagnosis and Gender. We built classification models that were allowed to use up to five factor loadings to discriminate between AD and NAD subjects. In a cursory first investigation using linear discriminant analysis, quadratic discriminant analysis (QDA), and support vector machines, QDA emerged as showing the best classification performance in leave-one-out cross-validation. Furthermore, the pc-spectra factor loadings did show better performance than the raw Fourier coefficients, so we concentrated on pc-spectra for further classification analysis. We built models for all combinations of time window (six windows for n = 39 subjects, two windows for n = 40), classification target ( Diagnosis and Gender ), and filter setting (F1 and F2), giving 32 model configurations. For each model, the optimal combination of five pc-spectra was selected by exhaustive search. Comparing the 16 F1 models with the 16 F2 models using a Wilcoxon signed rank test indicated a significantly better performance for the F1 setting (V=72.5, p=0.0094), so we concentrate on this filter setting for the further classification analysis. Fig. 3 compares the resulting classification accuracies. In Table 2 we give the confusion matrices for some typical models. In our opinion, the achieved accuracies indicate that the temporal structure of the activity envelope indeed shows correlation with the medical diagnosis. Furthermore, we see that at least at this level of analysis also gender specific differences in motion behavior are detectable, indicating possible further applications for activity envelope data. Finally, as M2 indicates, it seems possible to achieve even better results concentrating just on the night (which also would significantly re-

5 n = 39 M1 M2 M3 Performance Truth Class. AD NAD AD 17 2 NAD 3 17 n = 40 Truth Class. AD NAD AD 19 2 NAD 1 18 n = 39 Truth Class. FM F 18 2 M 2 17 M1 M2 M3 acc sens spec Table 2. Confusion matrices for selected models (M1: Diagnosis night.day, M2: Diagnosis night, M3: Gender night.day) and resulting values for accuracy (acc), sensitivity (sens), and specificity (spec). adjusted R Models for all subjects M4 Models for subjects with diagnosis AD afternoon cal.day core.night morning night night.day afternoon cal.day core.night morning night night.day Predicted MMSE True MMSE Fig. 4. Left: Adjusted R 2 values achieved for linear models of MMSE ( ) resp. MMSE (+) using five spectral components as predictor variables, selecting different data time ranges. Right: True vs. predicted MMSE values for model M4 ( night.day window and MMSE prediction), ( ): AD, (+): NAD. This model is able to explain 81% of the variance in MMSE (adjusted R 2 = 0.78, R 2 =0.81, F(5,33)=27.93, p<0.001) duce the time required for sensor data recording). However, as the appreciable variations in accuracy caused by adding another subject show, the models right now are not yet robust enough to warrant a reduction of recording time. For interest, in Fig. 3, right, we give the activity envelopes of the five pcspectra selected by model M1. These envelopes were computed by applying an inverse Fourier transform to the respective pc-spectra. Since phase information had been removed from the spectral features, there is no easy way to interpret these curves as indicating activity at a specific time of day further analysis is required to develop an understanding of the possible meaning of these reconstructed envelopes. Regression models for MMSE. With respect to predicting the MMSE, we found Fourier coefficients to perform better than pc-spectra. Based on the Fourier coefficients as predictor variables, we built linear models using either MMSE or MMSE as regression target. The latter target choice had been motivated by a preliminary residual analysis, indicating a nonlinear behavior of higher MMSE values, in agreement with the non-linear behavior of MMSE scores in large normative samples. For both targets, we built models with respect to different choices of time window, population (all subjects resp. only AD subjects) and filter setting (resulting in 96 configurations). For the filter setting we found that in contrast to the finding for classification the F2 setting was able to provide better results (using a Wilcoxon test on the adjusted R 2 for the different regression models). Predictor variables for a model were selected by applying the

6 step function of the statistics system R to an initially empty model. To avoid overfitting, we restricted the number of predictor variables to at most five. The values for adjusted R 2 of the resulting models are compared in Fig. 4, left. Note that we were able to achieve promising results; if we concentrate on just the AD subset, an even better fit can be achieved. A comparison of predicted and true MMSE values using the model M4 for the full population is given in Fig. 4, right. 4 Discussion In general, we think our results show that effects of AD are detectable in accelerometric motion protocols, even at early stages of AD (corresponding to high MMSE scores). It is important to note that the approach presented here does not aim to increase the accuracy of the diagnosis of AD, but to determine whether the diagnosis of AD is associated with changes in everyday motion behavior. Although the resulting surrogate markers of motion behavior do not easily offer an interpretation in clinical terms, the overall concept of an impairment of the temporal structure of everyday motion behavior agrees with a range of neurobiological and clinical studies in AD. The easily accessible assessment of early signs of forthcoming decline of motion behavior using automated sensors will have a major impact on the development and application of interventions to prevent or attenuate behavioral impairment in AD. Further analysis will concentrate on increasing the robustness: given the sample size, we do not think that enough data is available to identify sets of pc-spectra that are robust across a population. The effect on M2 of leaving out person A (see Fig. 3, left) clearly indicates high susceptibility to sample variation. Possible solutions include increasing sample size and integrating prior knowledge on activity structures; both options are subject of further research. References 1. Kearns, W., Algase, D., Moore, D., Ahmed, S.: Ultra wideband radio: A novel method for measuring wandering in persons with dementia. Gerontechnology 7(1) (2008) Grünerbl, A., Bahle, G., Lukowicz, P., Hanser, F.: Using indoor location to assess the state of dementia patients: Results and experience report from a long term, real world study. In: Proc 7th Intl. Conf. on Intelligent Environments (IE 2011), Nottingham, UK (2011) Tung, J.Y., Semple, J.F.L., Woo, W.X., Hsu, W.S., Sinn, M., Roy, E.A., Poupart, P.: Ambulatory assessment of lifestyle factors for Alzheimer s disease and related dementias. In: Proc. AAAI Spring Symposium: Computational Physiology, Stanford, CA (2011) 4. Folstein, M.F., Folstein, S.E., McHugh, P.R.: Mini-mental-state: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12 (1975)