Telehealth Applications
Dr Ladan Baghai-Ravary
Senior Telehealth Consultant, Aculab Plc
[email protected]
Automatic evaluation of health conditions affecting
voice/speech over the telephone
Aims:
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Easy to use
Global access
Objective and consistent
Frequent and flexible scheduling
Save time and travel costs
Scalable solution
Low costs
Telephony voice analysis system
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Speech from 25k volunteers over a few weeks
7 countries
25% diagnosed with Parkinson's Disease
Experiment:
➢ Say “aah” for as long as you can.
➢ Repeat the following sentences from the Grandfather passage:
“You wish to know all about my grandfather.”
“Well, he is nearly 93 years old.”
❖ Result: used Machine learning system for accurate identification of
Parkinson's Disease.
General telephony limitations
❖ Limited bandwidth
❖ Distortion
❖ Packet loss / packet loss concealment
… but telephone lines retain enough detail for the speech to
be understood - this includes the ability to detect many
abnormalities by the listener.
Acoustic parameters used analysis (GSM v PCM)
Spasmodic dysphonia (70 year old male, /a/, /i/, and /u/ vowels), before and after GSM coding
Mean Pitch
Jitter
Standard Devation of Pitch
Shimmer
Harmonics to Noise Ratio
Effect of Weak mobile network transmission
Comparison of network transmission in weak reception area with a high signalstrength area: female
male
sustained phonation /a/
female
sustained phonation /u/
male
Telephone recording:
Voice characteristics
59 year old male speakers, with perceptually similar voice:
“aah” waveforms
1 - Healthy
2 - With Parkinsons Disease
time
time
Telephone recording:
Speech characteristics
Spectrogram (frequency against time) of two 59 year old male speakers, with
perceptually similar voice.
1 - Healthy
“m
y
2- With Parkinson’s Disease
g r a
freq.
n ”
“m
y
g
freq.
time.
time.
r a
n”
Telephone recording:
Speech characteristics
The glide in the word “my” and the “g” are not properly articulated in the
Parkinson’s speech.
1 - Healthy
“m
y
2- With Parkinson’s Disease
g r a
freq.
n ”
“m
y
g
freq.
time.
time.
r a
n”
Telephone recording:
Speech characteristics
The red lines indicate the boundaries between predictable segments. The healthy
subject has more segments and with more change within each segment.
1 - Healthy
“m
y
2 - With Parkinson’s Disease
g r a
freq.
n ”
“m
y
g
freq.
time.
time.
r a
n”
Telehealth Application
Automated
Dialogue.
Record
speech
Automated
Analysis.
To monitor,
send results
Email
(SMS, Fax,
Download)
Clinician
or
Researcher
Demo: Tel: Oxford (01865) 521272
●
Thank you for calling the telehealth demo.
●
Please remember the following three digit number for later in the call: xxx
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“Please take a breath, then say aah for a few seconds.”
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Thank you; that's great. Now please say the three digit number you were
given earlier.
Finally, please repeat the following sentence after me.
●
Thank you. Your results will be emailed shortly.
●
That's the end of the call. Thank you and goodbye.
Results in an email
Subject:
Telehealth results
Date:
Mon, 12 Oct 2015 07:47:18 -0700 (PDT)
From:
[email protected]
To: [email protected]
Sustained phonation characteristic summary:
The mean pitch was 132.43 Hertz with a standard deviation of 2.37 Hertz.
The jitter was measured as 0.42 percent, with 3.77 percent shimmer.
The "noise to harmonics" ratio was 0.02.
Sentence dynamics summary:
The mean segment duration was 38.55 ms.
The mean dynamics level was 5.11 per ms.
Memory test:
The number to be remembered was "six, two, five".
It was remembered correctly.
End of telehealth report for recording "09357de4369b9bbe.9560".
Histograms for 2500 healthy participants’ voice
Jitter
Shimmer
Histograms for 2500 healthy participants’ voice
0.0377
0.0042
Within
normal
range!
0.02
Statistically Significant
❖ Sample range
❖ Sample size
Practical Challenges
❖ Are instructions clear?
Some mistakes with the our experiment:
➢ Say “aah” for as long as you can.
➢ Repeat the following sentences from the
Grandfather passage:
“You wish to know all about my grandfather.”
❖ Are instructions easy to follow?
❖ Require carer’s help or not?
❖ Is the environment exactly the same?
Conclusions:
❖ Cloud telephony provides a scalable, flexible, and powerful
framework. Such systems can provide universal access: they are
easy to use, low cost, and easy to develop and adapt. Combination
of voice and speech parameters could aid clinicians with early
diagnosis and/or monitoring of response to any treatment.
❖ Research challenges include adequate samples and range of
samples to be statistically significant.
❖ Automated system need to be tested with real patients.
Thank you
[email protected]