1 / 19

“ Analyze reaction of newborn to music ”

“ Analyze reaction of newborn to music ”. Maslovsky Eugene Vainbrand Dmitri Instructor: Kirshner Hagai. Winter 2005. Agenda. Concept Available Raw Data Project Goals Analysis Techniques Project Flow Results Conclusions Future research. Concept.

kata
Download Presentation

“ Analyze reaction of newborn to music ”

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. “Analyze reaction of newborn to music ” Maslovsky Eugene Vainbrand Dmitri Instructor:Kirshner Hagai Winter 2005

  2. Agenda • Concept • Available Raw Data • Project Goals • Analysis Techniques • Project Flow • Results • Conclusions • Future research

  3. Concept • Observations of newborn show that music influences their behavior. • Our project is part of a research on reactions of newborn to different music styles. • Can Engineering Analysis methods add new views and maybe resolvethis issue?

  4. Available Data • Six bio-signals were recorded from newborn while playing them music alternately: • Respiratory. • ECG. • EEG from four sources. • There is an online recorded movie.

  5. Project Goals • Study characteristics of basic bio-signals. • Study different signal processing and statistical methods. • Analyze given medical signals and define their connection to music playing.

  6. Analysis techniques • Signal processing basic methods: • DFT/FFT • Filtering • Window multiplying • Parameter estimation • AR model • Spectrogram • Statistical and Math methods: • Statistical hypothesis • Histograms

  7. Project Flow • Analyzing ECG Signal • Visual analyze in time and frequency • Basic Parameters analysis • Analyzing ECG in time domain • Statistical analysis of Amplitude and periods • Typical period shape analysis • FECG • Analyzing Respiratory Signal • Visual analyze in time and frequency • Basic Parameters analysis • Analyzing AR model

  8. First silent Second silent First music Third silent Second music Forth silent Third music Fifth silent First Silent Music Silent Definitions • Time segments

  9. Results • ECG: • Visual • In time domain signal is periodical. • In frequency domain signal is modulated pulse train with 50Hz bandwidth but there are no suitable parameters to analyze. • Windowing didn’t give other visual information.

  10. Result Mean energy: Music segments have slightly lower energy Hypothesis of equality - Denied with 0.99 probability

  11. Results • Statistical analysis of R-Amplitude, HR and HRV • HR • Similar histograms • Equality hypothesis not denied • Conclusion: No influence detected • HRV • Similar histograms except firsts 2 silent segments vs. all others • But Equality hypothesis not denied • Conclusion: No influence detected • R-Amplitude mean • Similar histograms • Equality hypothesis not denied • Conclusion: No influence detected

  12. Results • Statistical analysis of R-Amplitude, HR and HRV (cont) • R-Amplitude deviation • First two silent segments histogram is different • Equality hypothesis for First two silent segments vs. the others was denied with 98% C.L. • Conclusion: R-peaks amplitude became more unstable during the experiment. Can be related to music influence

  13. Results • Typical period shape analysis • Averaging on all periods shapes of each segment • Bigger difference between 2 first and rest segments

  14. Results Draft

  15. Results • FECG • The purpose: “edge influence” detection on HR • No edge influence was detected

  16. Results • Respiratory: • Visual impression: noise, no typical cyclicality, no typical amplitude. • Mean Energy: No results that show connection. • Signal processing: • There is a typical picture in frequency domain in 4Hz bandwidth but there no suitable parameters to analyze. • No effects from windowing and filtering. No visual correlation found.

  17. Results (cont.) • Auto Regressive model fitting • Goal – Estimate Frequency spectrum with auto regressive model. • We couldn’t find suitable results. • Maximum peak frequency variation was totally random

  18. Results (cont.) • Spectrogram analysis • No good visual results was achieved by spectrogram analysis

  19. Thanks Thanks for your attention!

More Related