Analysis of nighttime activity and daytime pain in patients with chronic back pain using a self-organizing map neural network.

There may be a relationship between sleep and pain in patients with chronic back pain. We collected day-time pain and nighttime activity data from 18 patients diagnosed with chronic back pain. The patients were followed for 6 days and 5 nights. Pain levels were collected every 90 min between 0800 hours and 2,200 hours using a computerized electronic diary. Activity levels were collected using a wrist accelerometer (Actiwatch AW-64). The Actiwatch sampled activity counts every 1 min. Patients were asked to wear the Actiwatch on their non-dominant arm. The pain level measurements were interpolated using cubic splines. A mean pain level was calculated for each period 0800 hours to 2,200 hours as well as for the 6-day period. The difference between the mean pain levels for the 6-day period and each 0800 hours to 2,200 hours period was calculated for each patient. Nighttime activity data were analyzed using the Actiwatch Sleep Analysis software. Correlations were calculated between the Actiwatch Sleep Analysis variables and the mean pain level differences for each patient and period. The correlation analysis was performed with SPSS 7.5. We were unable to show any significant relationships.A different approach to analyze the data was used. A Self-Organizing Map (SOM) Neural Network was trained using the original nighttime activity level time series from 10 randomly selected patients. Recall was then performed on all the activity level data. Correlations were calculated between the pain level variance for the 6-day period for each patient and the corresponding difference in the SOM output coordinates. The correlation was found to be r = 0.73, p < 0.01). We conclude that daytime pain levels are not directly correlated with sleep in the following night and that sleep is not directly correlated with daytime pain levels on the following day in this group of patients. There appears to be a correlation between the difference in nighttime activity levels and patterns and the daytime pain variance. Patients who experience large fluctuations in daytime pain levels also show a higher variability in their nighttime activity levels and patterns. Even though we were unable to show a direct relationship between daytime pain and sleep, it may be reasonable to assume that better pain control resulting in less daytime pain fluctuations can provide more stable nighttime activity levels and patterns in this limited group of patients. By using a neural network model, we were able to extract information from the nighttime activity levels even though a traditional statistical analysis was unsuccessful.