Browsing by Author "Sheikh, Mona A."

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    Fundamental limits in spike sorting
    (2007) Sheikh, Mona A.; Johnson, Don H.
    Spike sorting refers to the detection and classification of electric potentials (spikes) from multi-neuron recordings. It is a difficult but essential pre-processing step before neural data can be analyzed for information content. While several spike sorting algorithms have been proposed, our goal is to determine the ultimate limits of spike classification, and to characterize this error regardless of sorting algorithm. We have identified and incorporated three important factors that affect the sorting procedure - SNR, spike amplitude ratio and inter-spike correlation - into a signal constellation model to derive error probability bounds on any sorting procedure. We consider the cases of known and unknown time-of-occurrence of the spike(s) in question. We calculate spike timing error estimates in the case of unknown delay. Additionally, we derive a theoretical amplitude distribution for spike amplitudes at the electrode. Finally we introduce the idea of a non-gaussian "corruption" noise component that affects spike waveform.
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    Genomic Detection Using Sparsity-inspired Tools
    (2011) Sheikh, Mona A.; Baraniuk, Richard G.
    Genome-based detection methods provide the most conclusive means for establishing the presence of microbial species. A prime example of their use is in the detection of bacterial species, many of which are naturally vital or dangerous to human health, or can be genetically engineered to be so. However, current genomic detection methods are cost-prohibitive and inevitably use unique sensors that are specific to each species to be detected. In this thesis we advocate the use of combinatorial and non-specific identifiers for detection, made possible by exploiting the sparsity inherent in the species detection problem in a clinical or environmental sample. By modifying the sensor design process, we have developed new molecular biology tools with advantages that were not possible in their previous incarnations. Chief among these advantages are a universal species detection platform, the ability to discover unknown species, and the elimination of PCR, an expensive and laborious amplification step prerequisite in every molecular biology detection technique. Finally, we introduce a sparsity-based model for analyzing the millions of raw sequencing reads generated during whole genome sequencing for species detection, and achieve significant reductions in computational speed and high accuracy.
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    Universal microbial diagnostics using random DNA probes
    (AAAS, 2016) Aghazadeh, Amirali; Lin, Adam Y.; Sheikh, Mona A.; Chen, Allen L.; Atkins, Lisa M.; Johnson, Coreen L.; Petrosino, Joseph F.; Drezek, Rebekah A.; Baraniuk, Richard G.
    Early identification of pathogens is essential for limiting development of therapy-resistant pathogens and mitigating infectious disease outbreaks. Most bacterial detection schemes use target-specific probes to differentiate pathogen species, creating time and cost inefficiencies in identifying newly discovered organisms. We present a novel universal microbial diagnostics (UMD) platform to screen for microbial organisms in an infectious sample, using a small number of random DNA probes that are agnostic to the target DNA sequences. Our platform leverages the theory of sparse signal recovery (compressive sensing) to identify the composition of a microbial sample that potentially contains novel or mutant species. We validated the UMD platform in vitro using five random probes to recover 11 pathogenic bacteria. We further demonstrated in silico that UMD can be generalized to screen for common human pathogens in different taxonomy levels. UMDﾒs unorthodox sensing approach opens the door to more efficient and universal molecular diagnostics.