Journal: eLife

Article Title: Using mobile phones as acoustic sensors for high-throughput mosquito surveillance

doi: 10.7554/eLife.27854

Figure Lengend Snippet: ( A ) Schematic of experimental setup for recording a tethered mosquito using synchronized mobile phone audio and high speed video. Synchronization between audio and video is achieved on the order of microseconds, using a piezoelectric buzzer connected in parallel with an LED, and controlled with a microprocessor to produce the same temporal sequence of sound and light. ( B ) Overlaid spectrograms for female Culex tarsalis mosquitoes obtained independently using high speed video (magenta) and mobile phone audio (cyan), aligned to within 2 ms and showing a spectral overlap (blue) within 2 Hz across all time instances. The mobile phone data is noisy but faithfully reproduces the wingbeat frequency peak of 264 Hz and the first two overtones. ( C ), Wingbeat frequency distributions from video and audio (165 time instances each) have high overlaps, as measured by a Bhattacharya Coefficient (BC) of 0.95. ( D ) Signal-to-noise ratio (SNR) estimates over distance from a standardized sound source show that mobile phone microphone performance within a 100 mm radius is superior or comparable to high performance studio microphones. The pink line represents the actual source amplitude, with the pink-shaded region below indicating the region less than the actual amplitude, in which all acoustic measurements should lie. The gray-shaded band represents the range of sensitivities between the limits set by the two studio microphones used as reference standards. The SNR curves for all three phones lie mostly within this band, indicating that they perform in a range between the two reference microphones. ( E ) SNR over distance for the wingbeat sound produced by a tethered female Cx. tarsalis mosquito (normalized for a source amplitude of 45 dB), providing working limits where phones can detect the audio signal - 50 mm for the low end T-209 feature phone and 100 mm for the iPhone 4S and Xperia Z3 Compact smartphones. The gray-dotted line represents the actual amplitude of the mosquito sound in dB, as measured by the MXL991 reference microphone. The gray-shaded region below indicates the region less than the actual amplitude, in which all acoustic measurements are expected to lie. ( F ) Variation of the wingbeat frequency distribution sampled by 8 different phones is low compared to the natural variation within a population of lab-reared Anopheles stephensi females. Raw data are shown with overlaid box plots marking the inter-quartile range, black circles for mean frequency, gray vertical bars for median frequency, and whiskers indicating 5th and 95th quantiles. ( G,H ) The Jensen-Shannon divergence metric for base frequency distributions (G, lower left triangle) shows low disparity, ranging between 0.144 and 0.3, against a minimum of 0 for identical distributions. Likewise, the Bhattacharya distance (H, upper right triangle) shows high overlap, with values between 0.935 and 0.986, against a maximum of 1 for identical distributions. The brown-hatched areas along the diagonal represent blank cells, as distances are not shown for any distribution with respect to itself.

Article Snippet: We validated the MLE algorithm by classifying recordings from the iPhone 4S, Sony Xperia Z3 Compact, and Google Nexus One ( , ), by comparing them to the reference dataset collected using the SGH T-209 ( ).

Techniques: Sequencing, Produced

Journal: eLife

Article Title: Using mobile phones as acoustic sensors for high-throughput mosquito surveillance

doi: 10.7554/eLife.27854

Figure Lengend Snippet: ( A-E ) Comparison of power spectral density for synchronized simultaneous recordings of individual Culex tarsalis female mosquitoes using the MXL 991 studio microphone, the Apex 220 reference microphone and a mobile phone, taken at varying distances. The left column corresponds to the SGH T-209 feature phone, the middle column to the iPhone 4S iOS smartphone, and the right column to the Xperia Z3 Compact Android smartphone. ( A ) Superimposed averaged spectra show that all phones acquire wingbeat sound at a high signal-to-noise ratio at 10 mm away from the mosquito. ( B,C,D ) Overlaid spectrograms synchronized to within 20 ms in time show a near-perfect spectral match of within 5 Hz at each time interval, for the mobile phone microphone (red channel), MXL 991 (green channel) and the Apex 200 (blue channel), shown together as RGB images with intensity of colour corresponding to variations in power spectral density. Mobile phones strongly acquire mosquito sounds at 10 mm or even 50 mm, but their sensitivity drops sharply at distances of 100 mm. ( E ) Superimposed averaged spectra show that only the Xperia Z3 continues to acquire wingbeat sound at 100 mm away from the mosquito, albeit at low signal-to-noise ratio. The T209 feature phone picks up low-frequency noise between 300 and 600 Hz that overwhelms the mosquito frequencies, the iPhone 4S has low-noise acquisition throughout, and the Xperia Z3 picks up high-frequency noise above 1 kHz that leaves the mosquito frequency band relatively unaffected.

Article Snippet: We validated the MLE algorithm by classifying recordings from the iPhone 4S, Sony Xperia Z3 Compact, and Google Nexus One ( , ), by comparing them to the reference dataset collected using the SGH T-209 ( ).

Techniques:

Journal: eLife

Article Title: Using mobile phones as acoustic sensors for high-throughput mosquito surveillance

doi: 10.7554/eLife.27854

Figure Lengend Snippet: ( A ) Distribution of wingbeat frequencies for female mosquitoes of 20 vector species, for recordings obtained with the 2006 model SGH T-209 low-end feature phone (except Culex pipiens, Culex quinquefasciatus , and Culiseta incidens, recorded using iPhone models; and Aedes sierrensis , recorded with various phones). ( B ) (lower left triangle), Jensen-Shannon divergence metric for base frequency distributions. Distributions are spaced apart with high J-S divergence in most cases, with only four pairwise combinations having J-S divergence around 0.35 - the maximum divergence for the same species across different phones. ( C ) (upper right triangle), Qualitative classifiation of species pairs according to the possibility of distinguishing them using mobile phones — (i) no frequency overlaps, hence distinguishable by acoustics alone, (ii) overlapping frequency distributions, but not geographically co-occurring hence distinguishable using location, (iii)overlapping frequency distributions but distinguishable using time stamps, (iv) partially overlapping frequency distributions but no location-time distinctions, hence distinguishable but not in all cases, (v) indistingishable due to highly overlapping frequency distributions with co-occurrence in space and time. The brown-hatched areas along the diagonal represent blank cells, as distances are not shown for any distribution with respect to itself. ( D ) Confusion matrix for classification of acoustic data bootstrapped from the reference frequency datasets for twenty species, using the Maximum Likelihood Estimation (MLE) algorithm. Each column corresponds to a particular species, from which acoustic data is drawn for classification. The twenty entries in each column represent the fractions in which recordings from the species corresponding to that column are classified among all twenty species in our database. This classification is done for data taken from the reference distributions, and is compared against the same reference dataset, based exclusively on wingbeat frequency. Classification errors occur when a given species frequency distribution has overlaps with other species, and the confusion matrix reflects the inherent uniqueness or overlap between frequency distributions in our database. Colour scale showing fraction of recordings classified is the same for both D and E. ( E ) Confusion matrix for classification of test audio recordings, using the MLE algorithm. Test data were collected for 17 species, using different phones (some or all among Google Nexus One, Sony Xperia Z3 Compact, iPhone 4S) which were not used to construct the reference distributions (recorded using the SGH T-209). Each column corresponds to a particular test species, from which acoustic data are drawn for classification. The twenty entries in each column represent the fractions in which recordings from the test species corresponding to that column are classified among all twenty species in our database. Classification is based on both wingbeat frequency and a location filter, simulating the classification of randomly recorded mosquitoes of different species by users in field conditions. The resulting classification accuracies are significantly higher in this case, when compared to classification accuracies which do not consider location . Blank areas along three columns represent species for which test data from a different phone were not available. ( F,G ) Variations in base-frequency distribution (F) for field-recorded sounds corresponding to wild female Ae. sierrensis mosquitoes having a wide (about two-fold) variation in body size and wing area (G), showing small differences between individuals compared to the variation within each flight trace. The gray distribution at the top represents the species wingbeat frequency distribution for Ae. sierrensis , with the gray-shaded vertical band marking the range from 5th to 95th percentile of frequency for the species. All individual recordings lie completely within this range.

Article Snippet: We validated the MLE algorithm by classifying recordings from the iPhone 4S, Sony Xperia Z3 Compact, and Google Nexus One ( , ), by comparing them to the reference dataset collected using the SGH T-209 ( ).

Techniques: Plasmid Preparation, Construct

Journal: Heart

Article Title: Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis

doi: 10.1136/heartjnl-2021-320417

Figure Lengend Snippet: Summary of full-text studies

Article Snippet: Mutke et al 2020 , Prospective, multicentre; data from two trials (WATCH AF and DETECT PRO) Supported by industry , Secondary care N=1330 AF prevalence 47% , , iPhone 4S; Preventicus app; 60 s recording analysed using beat-to-beat variations via a non-linear rhythm analysis, signal quality check not performed , Single-lead ECG. Interpretation by two cardiologists with group consensus..

Techniques: Biomarker Discovery, Extraction